FUNDAMENTALS OF 5G MOBILE NETWORK ...

FUNDAMENTALS OF 5G MOBILE NETWORK Architecture, Requirement, Densification, Cooperation, and Resource Management

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NEXTEVOLUTION Branding Intellects

Rony Kumer Saha and Chaodit Aswakul 7/23/16

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ABSTRACT Based on the existing literature, in this progress report, we discuss on the 5G networks in terms of requirements, architectures, and key enabling technologies for capacity boosting. Major DenseNet elements and their cooperatio n techniques in the current and future networks are discussed elaborately. Both distributed -and centralized controlled networking paradigms for the 5G network architectures are discussed. Specifically, in the exiting distributed control networks, already proposed novel decoupling solutions for C-and U planes, uplink and downlink, baseband unit from the network node are discussed for possible architectural evolution towards the 5G networks. In the centralized control networks, several proposals on the application of the software defined networking concept to wireless environments for various wireless standards such as LTE, WiFi, and WiMAX are discussed. Because of the potential impact of radio resource management (RRM) functionalities on the network capacity, major RRM functionalities in the existing networks and beyond are detailed, and a novel proposal for the complete RRM implementation in the 5G networks considering multiobjectives and modular based functionality is introduced.

© RONY KUMER SAHA AND CHAODIT ASWAKUL

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ACKNOWLEDGEMENTS This study is funded by the 100th anniversary Chulalongkorn University Fund for Doctoral Scholarship and the Department of Electrical Engineering Chulalongkorn University PhD (EECU-PhD) Honors Program Scholarship.

Sincerely, Rony Kumer Saha (PhD ongoing) Chulalongkorn University, Thailand Chaodit Aswakul (PhD) Imperial College London, UK © RONY KUMER SAHA AND CHAODIT ASWAKUL

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TABLE OF CONTENTS Chapter 1

2

3

4

Title Introduction

Page 9

1.1 Motivation and Research Significance 1.2 Problem Statements 1.3 Objectives 1.4 Progress Report Outline

9 10 11 11

5G Networks and Capacity: Requirement, Technology, and Architecture

13

2.1 Introduction 2.2 5G Requirements 2.3 5G Enabling Technology for Capacity Enhancement 2.4 Approaches for 5G Network Architecture Evolution 2.4.1 Cell-Centric Architecture 2.4.2 Device Centric Architecture Distributed Controlled Network with C-Plane/U-Plane Separation Centralized Controlled Network with C-Plane/U-Plane Separation Distributed Controlled Network with Decoupling Downlink and Uplink Distributed Controlled Network with Decoupling Node and the Baseband Processing Unit 2.5 Summary

13 14 14 16 17 18 18 21 21 22

Elements of DenseNets

25

3.1 Macrocell 3.2 Picocell 3.3 Femtocell 3.4 Remote Radio Head 3.5 Relay 3.6 D2D Communications 3.7 M2M Communications 3.8 Summary

25 25 25 26 26 27 27 30

Cooperation in DenseNets

31

4.1 Introduction 4.2 Cooperative Communication 4.2.1 Relay Cooperative Systems 4.2.2 Node Cooperative Systems 4.3 CoMP Transmission and Reception Schemes 4.3.1 CoMP Cooperating Set 4.3.2 CoMP Scenarios 4.3.3 CoMP Deployments 4.3.4 CoMP Feedback Mechanisms 4.3.5 Backhauling and Overhead for CoMP 4.3.6 Uplink CoMP 4.4 Cooperative Interference Management 4.4.1 Interference in DenseNets 4.4.2 Inter-Cell RRM for Interference Management 4.5 Cooperative Carrier Aggregation and Scheduling 4.6 Backhaul Networks 4.6.1 Backhaul Networks and Deployment Choices 4.6.2 Practical Deployment Scenarios and Choice of Backhaul Solutions 4.6.3 Backhaul Networks for CoMP

31 31 31 32 33 34 34 34 34 35 36 36 36 37 39 42 42 44 44


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5

6

4.6.4 Advanced Technologies and Impact on Backhaul Networks 4.6.5 Backhaul Solutions for 5G Networks 4.7 Synchronization in HetNets 4.8 Multi-Antenna Systems and Cooperation 4.9 D2D Communications and Cooperation 4.10 Cooperation Strategy and Network Protocol Stack 4.10.1 Physical Layer 4.10.2 MAC Layer 4.10.3 Network Layer 4.10.4 Transport Layer 4.11 Summary

46 46 48 49 50 52 52 53 54 57

Wireless Software Defined Network

59

5.1 Introduction 5.2 SDN Concept 5.2.1 SDN Architecture 5.3 WSDN Architecture 5.3.1 WSDN Architecture on LTE Existing Core Networks SDN based LTE/EPC Architecture Option 01: Decoupling SGW and PGW in logical and data plane Option 02: Linking SDN controller with the MME Option 03: Integrating controller with SGW, PGW and MME functionalities 5.3.2 WSDN Architecture with Change on Existing LTE Core Networks Softcell 5.3.3 WSDN Architecture on Existing LTE Access Networks SoftRAN 5.3.4 WSDN Architecture on WiFi/WiMAX Networks OpenFlow Wireless 5.4 WSDN Implementation Requirement and Challenge 5.5 Summary

59 60 60 62 63 63 64 65 65 66 66 67 67 68 68 69 70

Radio Resource Management in HetNets

71

6.1 Introduction 6.2 RRM Functionalities in LTE Systems 6.2.1 Radio Bearer Control Radio Bearers Service Data Flow Bearer QoS Parameters QCI ARP GBR and MBR APN-AMBR UE-AMBR HSS 6.2.2 Connection Mobility Control Mobility Procedure Intra-LTE Mobility over S1 Intra-LTE Mobility over X2 6.2.3 Load Balancing Introduction Load Balancing Operations Load Balancing in LTE HetNets 6.2.4 Radio Admission Control 6.2.5 Dynamic Resource Allocation - Packet Scheduling 6.2.6 Inter-cell Interference Coordination 6.2.7 Inter-eNB CoMP 6.3 A Research Proposal: Multi-Objective Optimized Modular based Radio Resource Management for Evolved LTE-A Heterogeneous Networks

71 71 71 72 72 73 73 73 74 74 74 74 74 78 78 78 79 79 79 80 81 83 83 84 84


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7

Introduction RRM Functions RRM Objectives RRM Approaches RRM Modules 6.4 Summary

84 85 85 85 85 86

Conclusion

87

7.1 Discussion 7.2 Major Findings 7.3 Future Research Directions

87 88 89

References Appendix 1: List of Abbreviations

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LIST OF FIGURES Figure

Title

Page

1.1

5G network capacity attainment cube

10

2.1 2.2 2.3 2.4 2.5

5G network architecture evolution triangle Novel device centric network architecture New radio access network (RAN) architecture based on decoupling C-/U-planes New network architecture based on decoupling uplink and downlink New network architecture based on decoupling baseband processing units and physical nodes (a) full centralization of baseband processing units (b) partial centralization of baseband processing units

17 18 20 22 23

3.1 3.2

An example scenario for elements in DenseNet An example scenario for 5G DenseNet

28 29

4.1 4.2 4.3

32 33 35

4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17 4.18

Relay cooperative intra-cell CoMP CoMP schemes in heterogeneous LTE-Advanced networks (a) Scenario 1 - Homogeneous network with intra-site CoMP (b) Scenario 2 - Homogeneous network with high Tx power RRHs (c) Scenarios 3 - Network with low power RRHs within the macrocell coverage (RRH with own PCI) (d) Scenarios 4 - Network with low power RRHs within the macrocell coverage (RRH with no separate PCI) Interference in HetNets Network architecture for IC-RRM ABS based time-domain eICIC (a) between macroBS and femtoBS (b) between macroBS and CRE based PicoBS FemtoBS subframes are shifted by 3 symbol durations with respect to the macro UE’s Transmit power vs. subframe in ABS Capacity and delay characteristics of various backhaul transmission technologies Backhaul in HetNets for cooperation Physical implementation of LTE backhaul networks Backhaul solution scenarios: centralized (top) and distributed (bottom) Multi-antenna configurations CoMP for D2D communications Spectrum aggregation for DOFDM Cooperative links for MISO and MIMO Cooperative routing for backhaul networks (a) Node disjoint route, (b) link disjoint route, and (c) non-disjoint route

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9

Typical SDN architecture Idealized OpenFlow switch OpenFlow based LTE/EPC architecture Integration of SDN with S/P-GW Integration of SDN with MME Disruptive integration of SDN with MME Softcell network architecture SoftRAN architecture OpenFlow Wireless architecture

61 62 64 64 65 65 66 67 68

6.1 6.2 6.3 6.4 6.5 6.6 6.7

LTE EPS bearer hierarchy An example of SDF’s mapping to EPS bearers Operation of measurement and reporting for an event Measurement gaps in RRC_CONNECTED mode Functional architecture of SON load balancing Different uplink and downlink coverage points in HetNets Cell range extension procedure in HetNets

71 73 77 78 79 80 81

4.4 4.5 4.6


37 38 40 41 41 43 45 45 47 51 52 53 55 55 56

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LIST OF TABLES Table 3.1

Title

Page

A comparative framework of DenseNet elements

29


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CHAPTER 1 INTRODUCTION 1.1 Motivation and Research Significance The exponentially growing number of mobile users and an ever growing demand of high data rates impose challenges on the current cellular networks such as long term evolution (LTE) and its evolution in terms of high network capacity and wide coverage to meet up the near future demand of mobile users. Many advanced technologies such as carrier aggregation, multiple input multiple output (MIMO), enhanced interference coordination that have been considered for LTE evolution are seemed insufficient to meet the network capacity demand. In response, the standardization bodies, e.g., third generation partnership project (3GPP) has already introduced small cells in the coverage of a macrocell that result in shifting from the more traditional homogeneous networks to more advanced heterogeneous networks deployment. Homogeneous networks are characterized by the deployment of the same type of cells, e.g., macrocell/micro cell with the same high transmit power for all cells that span the coverage area uniformly following a particular cell planning strategy, e.g., the strategy can be based on frequency of the reusability of the same system bandwidth, called also cluster that defines the number of cells uses orthogonal channels out of the system bandwidth, over and over until the specific geographic area is covered. Because of high transmit power, typically up to 40W, the coverage of a macrocell is wide, e.g., few kms of radius. This causes mainly adverse impact on the cell-edge user’s throughputs who are far away from the macro base station (macroBS) because of the weak received signal-to-interference ration (SINR) and hence results in a degradation in overall system capacity. To improve the system capacity, low power BSs can be deployed in the coverage of a macrocell so that the distance dependent pathloss is reduced significantly, resulting improvement in per user throughput as well as the system capacity. There are several types of low power BSs have already been standardized for LTE-Advanced by the 3GPP such as picocell, femtocell, relay node, and remote radio head (RRH). Each of these small cells has different transmit power, cell coverage area, deployment environment (outdoor or indoor) from the macroBS and hence the term heterogeneous networks (HetNets). The most gain from HetNets can be achieved if the system bandwidth can be reused as many times as possible. This is why, even though small cells should be deployed with different frequency to overcome interference between cells and/or user equipments (UEs), operators usually prefer to co-channel deployment of small cells, where both the macrocell and the overlaid small cells can operate at the frequency, resulting more spectral efficiency per unit area. Since the capacity increases almost linearly with the increase of small cells as long as the interference level is within the reasonable range, it is desirable to deploy small cells as dense as possible that results in an extension of the so-called HetNets to dense HetNets or DenseNets to address the high capacity demand of the next generation mobile broadband – 5G mobile broadband. In DenseNets, an extremely large number of small cells is considered to be deployed where the coverage of small cells can overlap with one another. This raises the concern for significant interference from one cell to another and results in nonlinearity in achievable throughputs with small cells. A straight forward way to address the interference is to allocate orthogonal frequency to each small cell, however, as mentioned before this directly impacts the limited and highly expensive system bandwidth and hence the system capacity. The other alternative is to have a tight coordination among cells such that the same frequency can be reused among small cells with appropriate cooperation of network BSs. The cooperative communication has already been standardized by the 3GPP as coordinated multi-point communication (CoMP) and relay stations. CoMP uses mainly two strategies: joint transmission/joint reception (JT/JR) and coordinated scheduling/coordinated beamforming (CS/CB). The major difference between them is - in the former case, all BSs in the cooperating set transmit jointly user data to an UE or receive user data from multiple UEs, and in the latter case, only the serving BS transmits/receives the user data from an UE. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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However, in both cases, control signals are exchanged via backhaul networks among the cooperating BSs set. Hence, JT/JR is preferable mainly to CS/CB because of higher degrees of freedom from receiving multiple signals simultaneously and hence improve the user throughput almost linearly assuming interference has negligible impact and CSI and data are exchanged perfectly among BSs. From recent studies, it has been found that the spectral efficiency gain from coordination is multiplicative in some orders of magnitude on top of the gain achievable from HetNets. Hence, inevitably, it can be concluded that the use of cooperation principles will play a significant role on the 5G network capacity achievement.

1.2 Problem Statements

Spectrum Efficiency

Even though the 5G cellular is in its very early stage of research and development, and there is no specific directions on how the envisaged high capacity of 5G can be achieved, however based on most recent works on 5G networks, the capacity of 5G networks will most likely be driven by three major dimensions: spectrum aggregation, spectral efficiency techniques, and network densification (Figure 1.1) [HIS2012]. Since the current microwave frequencies are almost saturated, in order to increase the system bandwidth, millimeter wave (mmWave) spectrum bands are considered for the 5G networks because of usable high spectrum availability in these bands. Some practical evaluation on mmWave bands such as 28 GHz and 38 GHz have recently been carried out successfully. The mmWave bands are considered mainly for small cells with a provision for employing to the backhaul networks as well because of the stringent characteristics of these bands such as high path losses (hence smaller coverage, e.g., 200meters), line-of-sight (LOS) communications, and atmospheric attenuations.

Massive/Networked MIMO Cooperation technologies Advanced interference mitigation/alignment etc.

Spectrum Extension Exiting cellular bands New mmWave bands

k or

y sit en

ell l n coc cel io Pi emto stat F lay 2M Re RH /M R 2D D c. et

w et N

D

Figure 1.1: 5G network capacity attainment cube. To improve the spectral efficiency, advanced techniques such as massive MIMO, networked MIMO, enhanced inter-cell interference coordination (eICIC), and cooperative communications are the major technology enablers for the 5G capacity. Massive MIMO where hundreds or even thousands of antennas can be deployed either centrally at the BS or in distributed manner in the cell coverage depending on the scenarios and requirements. Further, networked MIMO is also an alternative implementation to the massive MIMO where a group of BSs with multiple antennas are coordinated to form one super BS, employing cooperation techniques such as JT/JR, and more spectral efficiency can be achieved than the massive MIMO, has been shown in recent studies. The third dimension is the ultra-dense heterogeneous networks that take advantages of the reuse of spectrum and shorter distance from small cell BSs and UEs. Consequently, in this research, we aim at exploring these three dimensions and analyzing the capacity performance of 5G networks.


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1.3 Objectives In order to address these dimensions, we consider the DenseNets in the first phase where we will analyze the capacity of DenseNets with cooperative communications employed. We will then apply the massive MIMO and the networked MIMO to boost the capacity further and will derive the capacity bounds for each case. Finally, mmWave spectrum will be employed to the small cells to enhance the capacity even a step further of 5G networks. In each phase, we will verify the concep ts analytically and will simulate the relevant scenarios to analyze the capacity performance of 5G networks. As part of phase 1, in this progress report, an extensive level of literature review is carried out with relevant illustration, specifications, and mathematical expressions. Specifically we address the followings: 

5G networks are discussed in terms of requirements, major enabling technologies for capacity boosting, and architectural evolution both in distributed and centralized approaches.



In distributed networking architecture, we discuss major 5G DenseNets elements and their involvement with an example DenseNet deployment architecture.



Cooperative communication principles and practices to address the various issues in DenseNets is discussed, mainly the capacity improvement, interference coordination, and backhaul requirements.



In centralized networking architecture, wireless software defined network (WSDN) is discussed along with mentioning various challenges and requirements.



Since radio resource management (RRM) is an integral part of capacity performance particularly for 5G DenseNets, we discuss briefly major RRM functionalities considering LTE evolution as standard and points out issues that may need to be considered in DenseNets scenario. We also include a proposal on multi-objective and modular based RRM implementation for 5G networks.



Finally, possible future research directions are pointed out in a concluding remark of the progress report.

1.4 Progress Report Outline The progress report is made up of a total of seven (7) chapters, including abstract and acknowledgements before and references after all the chapters have fitted in the report. In the following, we mention the topics that each chapter will eventually cover. In chapter 1, i.e., in this introductory chapter, we explain the major drivers and significances for carrying out the dissertation research on the capacity analysis of 5G networks through pointing out the current-state-of-the-art user demands and network capacity with a projection on the network capacity requirements in the near future. We state the research problem scopes for capacity analysis in terms of three dimensions, namely dense HetNets, spectrum efficien cy, and spectrum aggregation. We set the research objectives that are considered to be carried out in several phases, and specify the information that we discuss in this progress report chapter-wise based on literature review so far as a part of the first phase of the final dissertation contribution. In chapter 2, we address the 5G networks, including the necessity of 5G through reviewing current status of the cellular mobile networks and user demands. We identify key enabling technologies that can help boost the 5G network capacity by exploiting the Shannon’s capacity formula in HetNets. The possible ways of how these enabling technologies can be © RONY KUMER SAHA AND CHAODIT ASWAKUL

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applied through exploiting novel network architectures that have been proposed in literature recently as feasible architecture evolution for 5G networks are discussed. In addition, we propose three major dimensions for the network architecture evolution and explain a number of architectural changes based on two of the three dimensions, implying a shift from the cell centric to device centric architecture design by considering decoupling of many existing networking aspects and by exploring both centralized and distributed network controls. In chapter 3, we discuss the major elements of DenseNets such as macrocell, picocell, femtocell, RRH, relay station, and D2D in terms of their characteristics, functionalities, deployment strategies, etc. A comprehensive comparison of the DenseNets elements is provided. An example scenario of how these elements integrated in a typical cellular network is illustrated and in addition an example 5G DenseNet deployment scenario is also illustrated for visual comprehension. In chapter 4, we address particularly the cooperative communication on current distributed -controlled cellular networks and detail out the existing cooperation techniques in 3GPP LTE networks and their advancements to meet the upcoming 5G networks. Various cooperative mechanisms are discussed for the current networks as well as future networks considering possible technologies and scenarios. Requirements from cooperation such as backhaul networks, etc. are also exploited. Cooperation in new enabling technologies such as D2D communications are discussed along with the impact of cooperation on various layers are explored layer-wise. The performance of cellular networks with or no cooperatio n employed on the networks are discussed elaborately based on the existing literature. In chapter 5, we present a new centralized-controlled networking model called software defined networking (SDN) in wireless networks. The basic concept of SDN is described and benefits are highlighted. A number of recent architectu ral research proposals on the application of SDN in wireless networks, also called as wireless software defined networking (WSDN), mainly on wireless standards such as LTE, WiMAX, and WiFi are reviewed extensively and discussed concisely. Several Major research challenges and requirements in wireless networks for the application of SDN are pointed out at the end of the chapter. In chapter 6, we discuss on major RRM functionalities of traditional cellular networks in addition to advanced intelligent networks such LTE-Advanced and beyond systems. Because of having it direct impact on the overall system capacity, we identify and discuss most of the basic with a number of advanced RRM functionalities in the context of HetNets. As HetNets will also be considered in the 5G networks, we also propose a complete RRM functionality implementation model for the future 5G networks. In chapter 7, we carry out an overall discussion that incorporate major findings from the literature review documented in this progress report. We also point out promising, but more focused future research directions in line with the overall accomplishment in terms of the final dissertation contribution.


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CHAPTER 2 5G NETWORKS AND CAPACITY: REQUIREMENT, TECHNOLOGY, AND ARCHITECTURE

2.1 Introduction The International Telecommunication Union Radio Standards Sector (ITU-R) in January 2012 approved the International Mobile Telecommunications-Advanced (IMT-Advanced) Specifications of Fourth Generation (4G) Terrestrial Mobile Telecommunication [CWA2014]. However, continual increase in the number of mobile user, high end -user data rate, increase usage of real-time communication, rich multimedia features, trendy applications, large video streaming, smart end devices, high mobility requirement, etc. make the existing cellular mobile networks difficult to meet the demands from mobile users and initiates the investigation for next generation, Fifth Generation (5G) mobile networks. According to [CIS2010] between the years 2009 and 2014, the global traffic of mobile networks increased by 66-fold with a yearly growth rate of 131 percent. However, in response, the peak data rate increased only by 55 percent yearly from the third generation (3G) to the fourth generation (4G) mobile technologies [VGU2014] . This implies a clear indication of huge deviation between the demand from the mobile users and the capability that the network can provide. Published by Cisco, according to quantitative annual visual network index (VNI) [CIS2014], the data usage will continue and to meet up with the demand that the network will experience by 2020, an incremental approach towards meeting the demand will not be sufficient [JGA2014]. The IP data usage by wireless networks will increase from under 3 exabytes in 2010 to over 500 exabytes by 2020 [JGA2014]. By 2017, 7 trillion wireless devices will serve 7 billion people as predicted by the Wireless World Research Forum (WWRF) which implies a 1000-fold wireless devices are expected to serve the world’s population [WWR2009]. Further, the recent concern about Internet of Things (IoT) to make life more efficient, safe, and comfortable, defined as communicating machine-centric Internet is expected to be dominated over the current practice of human - centric Internet in future and are expected to be in the 5G networks [CWA2014] with a total of 50 billion connected device are forecasted by 2020 [AHA2008]. LTE subscriptions are predicted to surpass 1.3 billion by 2018 [INF2013]. LTE-Advanced as 4G systems (3GPP release 10) has been deployed already in some parts of and are expected to be deployed around the world [CWA2014]. As of 2013, more than 200 commercial LTE networks have been in operations [BBA2014]. The major drawbacks that the users facin g from current networks are non-uniform and low data rate, non-unified service provision and quality of experience (QoE), poor end-to-end performance, weak indoor coverage, insufficient high mobility performance, and high cost per bit transfer. From the network operator’s side, mainly provision for high network capacity, low latency, high spectral efficiency, large spectrum availability, and high energy consumption. To address the these challenges and demands, 3GPP has alread y taken initiatives through LTE evolution by introducing many advanced features and technologies such as enhanced intercell interference coordination (eICIC), small cells based Heterogeneous networks, full dimension MIMO, etc. 3GPP release 12 standardization, also called LTE-B, as a step towards LTE evolution is expected to be finished by 2014 [EHO2014] and future evolutions of LTE are also on-going. Nonetheless, with the existing networks, it is not possible to address these aforementioned challenges both from user and network operator’s sides. In this context the 5G mobile networks technologies are expected to be standardized around 2020. Hence to address the 5G requirements and technologies, there have already been a great attention seen in the research communities. 5G collaborative projects such as METIS [FPE2012], 5GNOW [FPE2012] have already been engaged and industry is carry out preliminary standardization activities on 5G [JGA2014]. For example, METIS is the EU flagship 5G project the approach of which builds on the evolution of current technologies with an integration of new radio concepts to meet up new future challenges beyond 2020 that the current network cannot support by employing advanced technology such as ultra-dense networks, massive MIMO, device-to-device communications, etc. [AOS2014].


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Public private partnership for 5G infrastructure has recently constituted in Europe to help promote further the 5G research [INF2013].

2.2 5G Requirements Even though there is no exact specification what 5G will encompass, based on what most people agreed so far, as compared to the 4G networks, 5G networks should attain the followings [JGA2014] [CWA2014] [EHO2014]:                

1000 times the system capacity 10 times the spectral efficiency and energy efficiency 25 times the average cell throughput 10 times longer battery life time 5 times reduced latency 1000 times aggregate data rate (or area capacity) 100 times (0.1 Gbps to 1 Gbps) cell-edge rate 10-100 times higher user data rate 10-100 times higher number of connecting devices 10 Gbps for low mobility and 1 Gbps for high mobility peak data rate 100 times reduction energy per bit and cost per bit 10-100 times cheaper mmWave spectrum (as compared to below 3GHz spectrum) 10-100 times cheaper small cells than macrocell s 350 km/hr to 500 km/hr mobility support than only 250 km/hr for 4G networks Connect the whole world Achieve seamless and ubiquitous communications between anybody, anything, anywhere, anytime, and anyhow.

2.3 5G Enabling Technology for Capacity Enhancement Using Shannon’s capacity formula and considering interference effect, the system capacity of a macroBS for a point-topoint communication can be given as,

 PRsys Csys  Bsys log2 1   IR  NR sys sys 

   

(2.1)

assuming that the system capacity is approximately the same as the throughput at the UE. Here,

PRsys , I Rsys , and N Rsys

are respectively the received signal power, the interference power and the AWGN noise at the UE. Bsys is the system bandwidth. Now if we consider multiple antennas at the BS and at the UE, i.e., MIMO, and there are M parallel streams in the link, where M=min {n t, n r} is the spatial multiplexing gain with n t and n r denote respectively the number of antennas at the BS and the UE, then

 PRu Csys  M Bsys log2 1   IR  NR u u 

  (2.2)  

Consider that we deploy small cells in the coverage of the macrocell and small cells are operated at the same system bandwidth (BW), i.e., the macroBS BW, which is literally termed as co-channel deployment of small cells in heterogeneous networks. If the same BS BW is reused K times in the coverage of small cells, the system capacity becomes, © RONY KUMER SAHA AND CHAODIT ASWAKUL

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 PRu Csys  K . M . Bsys . log2 1   IR  NR u u 

   

(2.3)

Since in the practice, there are some co-channel interference effect from the cross-tier (between macro tier and small cell tier) and also intra-tier, particularly for the small cells because of dense deployment in the coverage of the macrocell, the capacity in HetNets should be scaled by a factor, what we refer it resource reuse scaling factor, Q. Hence, the system capacity of HetNets for a single macroBS overlaid by small cells can be given by,

 PRu Csys  (Q . K ) . M . Bsys . log2 1   IR  NR u u 

  (2.4)  

Note that we implicitly consider that the received power is the same at an UE irrespective of whether the UE is served by the macrocell or small cell i.e., we have not considered the effect of difference in transmit power of macroBS and small cells for simplicity and consistency in the assumption. The assumption is reasonable enough, in a sense that even through the transmit power of the macroBS is much higher than a small cell BS, the distance between the link distance between an UE and the macroBS is higher and hence higher path loss compared to that in the case of small cells. Further a macro UE face more fading effect from mobility as an outdoor user, whereas an UE in indoor is mostly stationary or low mobility pedestrian users and there is a high probability of having LOS link between an UE and the small cell BS. Hence, the low transmit power in a small cell is compensated by the good channel condition at th e receiver. If we now analyze the system capacity formula, the possible ways to improve the system capacity in HetNets are as follows: The capacity is directly affected by the parameter Q, the resource reuse scaling factor. The more the dense HetNets, the higher the probability of interference. Hence the value of Q should be adapted based on the degree of density of small cells deployments. Note that the value of Q lies between 0 and 1, with 1 denotes no interference effect. Also the capacity is directly affected by the parameter K, the resource reuse factor. The more the dense HetNets, the higher the system capacity. Hence with more reuse of the spectrum by increasing small cells deployment, more capacity can be achieved. The capacity is also directly affected by the parameter M, the spatial multiplexing gain. The more the symmetric number of antennas both at the transmitter and the receiver, the higher the system capacity. Since more antennas cannot be implemented at the UE, multi user (MU) and implementing a massive number of antennas at the mcroBS, large spatial multiplexing gain can be achieved and hence is the capacity. This implementation of large number of antennas, typically 100 or more, at the BS or distributed manner is called massive MIMO or large antenna systems and hence, is considered as one of the key enabling technology for 5G network capacity attainment. In addition to massive MIMO, other advanced technologies such as networked MIMO, distributed antenna systems (DAS) can be used. In networked MIMO, network BSs cooperate each other, forming a cooperating set of BSs also called as cluster. The cooperating BSs acts as a multiple antenna systems by exchanging information and data via backhaul networks in distributed manner. Multiple users are coordinated at a time by the cooperating BSs based specific coordination strategy. Unlike massive MIMO, networked MIMO can gain advantage from spatial diversity and hence can improve throughput at the UEs, particularly in the cell edge areas. In DAS, multiple radio heads are deployed remotely which are controlled centrally by a baseband signal processing system. The radio heads are connected to the central system via high speed backhauls and can cover a large area even with the same transmit power as that of a macroBS. We will discuss in detail the multi antenna systems in the later chapter.


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Also the capacity is directly proportional to the parameter Bsys, the system bandwidth. The more the system bandwidth, the higher the system capacity. Hence, existing technologies such as carrier aggregation can be exploited either contiguously from the same band or non-contiguously from different bands to increase the system BW. There is around 600 MHz microwave spectrum for cellular systems allocated among network operators [ZPI2011]. To get more spectrum for aggregation from microwave spectrum, there are two ways left, one of which is the repurposing of available television spectrum with up to 80 MHz, and the other way is to apply cognitive radio concept. However, the high hopes from cognitive radio diminishes from not fully willing to cooperate with the secondary users and hence, makes the secondary users gaining spectral efficiency difficult. Hence, since most of the frequencies below 3GHz is almost utilized, the only way to increase the spectrum is aggregate spectrum from millimeter wave spectrum (mmWave) ranging from 3 to 300 GHz. A number of band are available with high bandwidth such as local multipoint distribution service at 28-30 GHz, the license-free band at 60 GHz, the E-band at 71-76 GHz, 81-86 GHz, and 92-95 GHz. From theses mmWave spectrum, several tens of GHz would be available for 5G network capacity [FBO2014]. Lastly, the capacity varies logarithmically with parameters PRu and I Ru , respectively the received signal and interference powers at an UE, considering that the UE channel is interference limited and hence the effect of noise is neglected. Advanced adaptive power control and cooperation communication such as coordinated multi-point (CoMP), time-domain based enhanced inter-cell and interference coordination (eICIC) can be applied to improve the signal strength and mitigate the interference at an UE. In CoMP, joint processing (JP) and coordinated scheduling/coordinated beamforming (CS/CB) are the two major techniques that can be used to address these issues. In JP, all BSs transmit the same data to an UE, such that the inter-cell interferen ce signals becomes desired signals and at the same time by combing multiple copies of the same signal diversity gain in received signal can be achieved. Hence both received power improvement and interference mitigation are achieved. In CS/CB, only the serving BS sends the user data and all other cooperating BSs simply coordinated the resources such that the interference at the UE is minimal. Even though this strategy does not explore spatial diversity, the overhead in the backhaul is reduced as compared with the JP where the user data are first sent to all the cooperating BSs before transmitting to the UE and hence results in more backhaul overhead. We also explain in detail these strategies in the later chapter.

2.4 Approaches for 5G Network Architecture Evolution There are mainly three dimensions for the evolution of 5G network architectures, as shown in Figure 2.1, such as the nature of network node (cell) centering what the network architecture is built, e.g., the generic nature of nodes would be based on the minimum number (an UE) to the maximum number (a macrocell ) of simultaneously served users, and the nature of network control, e.g., fully centralized to fully distributed with what nodes in the network are governed and managed, and network performance with what the performance of the network is measured, e.g., extremely low to extremely high in terms of technically, for example, capacity, latency, data rate, etc., and economically, for example, return-on-investment, cost of network deployment, operation and maintenance. In this chapter, we will emphasis on the first two dimensions, and third dimension, network performance, will be explored in later submission.


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NEXTEVOLUTION

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Maximum served users Fully distributed Extremely high

Ex

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tre me hig ly h

pe Net rfo w rm ork an ce

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tre me ly low

Figure 2.1 5G network architecture evolution triangle.

2.4.1 Cell-Centric Architecture In traditional cell-centric architecture, an end user equipment (UE) gets service from the network by making connectio n establishment both in uplink and downlink that carry traffic for control and user data following a either frequency division duplex (FDD) or time division duplex (TDD). All BSs have usually the same transmit power for homogeneous network and BSs are deployed and scaled, if needed for capacity demand, following some cell planning strategies, traditionally based on the frequency resource reuse factor. The network is controlled in distributed manner where each BS takes the responsibility of all UEs under its coverage and handing off to the adjacent one following a handoff principle with the assistance of mobility management entity (MME). All layers, i.e., Layer 1 or baseband, layer 2 and layer 3 functionalities are performed at the macroBS. The UE is under the sole control of the BS. The user-plane (U-plane) and control-plane (C-plane) functionalities of an UE are performed locally at the BS. The UEs are controlled by the network employing both the C-plane and the U-plane connectivity governed by the same entity, i.e., the functionalities for both planes are not governed by different entities simultaneously. For exampl e, when an UE initiates a service request to a BS, from the network and cell selection procedure to the end of the service session, all control signaling and user data transferred from (to) the BS to (from) the UE over the whole session are governed by the same BS as long as the UE does not reselect or handoff (while moving) to a different BS. After successfu l handing off, the new BS only governs the UE, and all functionalities regarding the control and data planes are transferred to the new BS from the old BS. In short, an UE cannot be served for the control plane and the data plane functionalities by different BSs at a time. To boost the capacity in traditional homogeneous networks, small cells can be deployed in the coverage of a macrocell such that the same carrier can be reused in the small cell tier. Small cells are characterized by low transmit power to cover a small area in the macrocell particularly in areas of hot spots, indoors, and cell-edge areas. Several varieties of small cells have been proposed in different releases of 3GPP and most of them have been deployed or is expected to be deployed soon, e.g., femtocells, picocells, relay stations, and RRH each with different transmit power ranges from around 23 dBm to 46 dBm for serving specific purposes on top of the macrocell . A network with deployed with BSs with different © RONY KUMER SAHA AND CHAODIT ASWAKUL

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transmit power and different cell coverage are termed as heterogeneous networks (HetNets). All these cells are explained in detail in a later chapter.

2.4.2 Device Centric Architecture In device centric architecture, a device should be able to communicate with the network though multiple possible sets of nodes in a HetNet by exchanging multiple flows [FBO2014]. Hence, a set of nodes provides connectivity to a device and the functions of these nodes should be contextualized to the specific device during the session. To address this device specific architecture for 5G network, many changes on the existing networks may need to be addressed, e.g., decoupling in C-plane and U-plane, serving uplink and downlink by separate nodes, centralization of baseband processing unit by separating the processing hardware unit from the node (BS), cooperative communication between multi -nodes, redefinition of architecture for ad-hoc type communications such as D2D, sensor networks, etc. An example device centric architecture is shown in Figure 2.2 [FBO2014]. In the followings, we will address these approaches, specifically the decoupling approaches in detail enough to address the network architecture evolution for 5G net works. Fully/partially* centralized baseband

* For partially centralized baseband L1 will not be included in the centralized unit

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Figure 2.2 Novel device centric network architecture.

Distributed Controlled Network with C-Plane/U-Plane Separation In distributed HetNets, the small cells can be deployed with C-plane and U-plane separation. In this configuration, the Uplane is served by the small cells and C-plane is served by the macrocell for an UE. A small cell simply provides U-plane traffic and the macrocell provides the C-plane to an UE. Small cells are not configured with cell-specific signals (channels), e.g., synchronization signals, cell specific reference signals (CRS), system information blocks (SIB), etc. and all radio resource control (RRC) connection procedures are provided by the macrocell. Hence, these small cells are also termed as Phantom Cells [HIS2012]. Small cells can be assigned with high frequency band such mmWave frequency band and to provide mobility requirement, the macrocell can be assigned with existing cellular spectrum with mostly less than 3 GHz. This can help address the high © RONY KUMER SAHA AND CHAODIT ASWAKUL

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capacity need for 5G networks by small cells, and at the same time, reduce the number of hand -offs because of large coverage of the macrocell . There are two scenarios for the realization of the C-plane and U-plane split: (1) small cells with baseband processing located at the macroBS and (2) small cells with independent baseband processing. In scenario 1, all baseband processing of small cells and the microcell are performed at the macroBS. Small cells simply carries the U-plane traffic to an UE. Though physically separated, logically both the macroBS and the small BS seemed as a single entity. This scenario can be applied to the 3GPP LTE carrier aggregation deployment for RRHs with the centralized baseband processing at the macroBS [GTS2012]. In this scenario, there is strict requirement for high speed and low latency backhaul such as optical fiber or LOS microwave connection between a small cell BS and the macroBS, and the small cells have to be deployed in the coverage of the macrocell because of common baseband processing located at the macroBS. Because of the common U-plane processing, there is a limitation to the maximum number of small cells that can be deployed in the macrocell coverage. In scenario 2, small cells have their independent baseband processing systems, and hence they appear as separate entity from the macrocell. Similar to scenario 1, all C-plane functionalities are provided by the macrocell by transferring signaling information via a new interface to a small cell. This configuration of C-and U-planes overcome the limitation of the scenario 1 because of the local baseband processing at the small cell itself and hence, there is no restriction from macroBS baseband processing limitation. Hence this scalability feature help scale the network capacity, i.e., the capacity can be increased with demands with further deployment of small cells. Further, because of independent baseband processing of a small cell, a small cell can communicate with multiple macroBSs, particularly at the cell edge areas in order to enhance the cell -ed ge user throughput. This configuration can complement or overcome the necessity for the existing CoMP strategies. By cooperating with multiple macroBSs via backhaul interface, instead of serving an UE at the cell-edge by the serving macrocell as in the case of CS/CB CoMP, the UE can be served by the small cell so that high distant dependent path loss from t he serving macrocell can be overcome by serving the UE from the small cell at short distant with good channel condition to improve throughput at the UE. In this case, only an addition to the existing networks is to define and deploy a new interface between a small cell and the cooperating macrocell s. In addition, the huge overhead in the backhaul from user data exchange between cooperating nodes to explore diversity to improve the cell-edge UE throughput in the traditional JP CoMP can also be overcome since small cells operate at different frequency than the macroBSs. Hence, no inter-cell interference experienced at an UE at the macrocell edges and so is no need for JP CoMP. Further, cooperating among multiple BSs can also help balance the load for handling the C-plane functionalities of a small cell among nearby by macroBSs in the cooperating set which improve the flexibility in small cells deployment densely. The other advantage from this configuration is from the low backhaul requirements. Since the baseband is processed at the small cells, stringent low delay and high speed requirements are not necessary and hence using non-ideal backhaul such as NLOS microwaves, DSL cables can be used. Based on the scenario 2, a new radio access network (RAN) architectu re that employs C-/U-planes separation has been proposed in [HIS2012] as shown in Figure 2.3.


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Operator radio access networks

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e U-

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pla

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Figure 2.3 New radio access network (RAN) architecture based on decoupling C-/U-planes. The C-plane is the small cell is managed by the macroBS and the U-plane is served by the small cell that requires a new data path (S1-U) as backhaul to connect to the core network. There is no need for C-plane interface (S1-C) for the small cell to the core network since it is handled by the macroBS. The macroBS is connected to the small cell via a new interface, let’s denote X3. A new carrier type possibly with high frequency such as mmWave band is needed for the small cell. Note that choosing for high frequency such as mmWave spectrum for small cells has an apparent impact on addressing the prospective very high potential enabling technologies for 5G capacity boosting such as more spectrum aggregation can be possible in the mmWave bands, in GHz level bandwidth. Secondly, mmWave frequencies have high path loss smaller coverage that fits with the small cell coverage and hence more densely small cells can be deployed. thirdly, mm wave length is attractive for massive MIMO design to enhance the spectral efficiency since more antennas can be located with reduced antenna array size and large gain from beamforming with large number of antennas can be achieved that can supplement the high path loss from mmWave spectrum. Also a new cell discovery for small cells is needed. This is because of high path loss at mmWave frequency, and hence antenna directivity is used to boost the signal in the direction of communication link. However, cell discovery face problems from directivity. Hence to overcome this problem at mm Wave in small cells, the small cell discovery at mmWave can be done in a number of ways [QLI2013]. One way is that the macroBS handles the discovery process. In this case, the macroBS knows the small cell coverage and also UE location, informs the small cell as the UE heads towards the small cell such the small cell can steer its beam towards the UE and also inform the UE so that the UE can also steer its antenna beam toward the small cell BS. The other way would be to implement both the small cell and the UE with the capability to operate on below 3GHz and mmWave frequencies. In this case the cell discovery is provided at below 3GHz, and once the UE gets connected to the small cell, the small cell can transmit user data at mmWave band by steering its anten na’s beam towards the UE. In this new RAN [HIS2012], the macrocell first sends RRC message to an UE to measure the channel condition between the UE and the small cell for connection establishment. The UE then after measuring the channel, reports it to the macrocell. The macrocell then ask the small cell via backhaul for its preparation to serve the UE. Once the small cell confirms with a positive response for its preparation to the macrocell, the macrocell then initiates to the UE a RRC connection set-up request from the small cell to the UE. The UE then requests for connection to the small cell using random access (RA) procedure with preamble. The small cell then responses to the UE’s RA request and the UE then © RONY KUMER SAHA AND CHAODIT ASWAKUL

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sends the RRC connection set-up (between the small cell and the UE) confirmation message to the macrocell. The user data is sent directly via S1-U interface from the core network to the small cell, and the small cell then sends user data to the UE.

Centralized Controlled Network with C-Plane/U-Plane Separation Software Defined Networking (SDN) is a new networking paradigm and one the most widespread topics now-a-days in both wired and wireless networking environments because of its unique network programmable capability. SDN provides flexibility, scalability, simplicity, and evolbility in network operation, control, an d management with a software platform. It principles on separating the network intelligence to a logically centralized entity called controller from the network processing units called data plane switches. Even though SDN principle was initially evolved around wired networks, recently, it has got significant momentum in wireless networks as well. Numerous research projects on wireless SDN (WSDN) are ongoing, and research approaches are proposed to address several issues of WSDN. WSDN is still in its very early stage of design and development with a very few available research contributions that provide insights into the domain. The physical infrastructure availability for carrying real-time experimentations is limited since deploying wireless networks such as LTE costs huge, and network operators usually do not allow their production networks to use for research experimentation purpose. Moreover, SDN itself needs many new features and functional elements such as network hypervisor, application programmable interface (API) enabled switch for sound bound etc., and many of them are not commercially available in the market. Hence, either these elements would have to be created by own or be waited for the standardized bodies in order to perform real-time experimentation on WSDN. Considering these aspects, it is worthy enough to study the feasibility of WSDN implementation taking into account of implementation specific resource availability and several fundamental challenges of WSDN that are to be addressed before considering real-time experimentation. In a later chapter, we would like to address these issues. Specifically, we will present the fundamental concept of SDN and its extension to wireless networks along with mentioning challenges and resource requirements for implementing a complete WSDN.

Distributed Controlled Network with Decoupling Downlink and Uplink For HetNets, in the downlink, different BSs have different transmit power with a difference of more 20dB between a macroBS and a femtoBS and their coverage varies accordingly. However, in the uplink, the transmit power from a mobile UE is almost the same. This disparity in downlink transmit power between BSs in the downlink and almost the same transmit power from UEs causes significant effect on the BS’s coverage, e.g., the maximum SINR downlink coverage and the maximum SINR uplink coverage for different BSs are not the same [JAN2013]. In the downlink, the maximum SINR regions follow the transmit power of each BS, i.e., macroBS with the largest, followed by picoBS, and then femtoBS. However, this not the case in the uplink, where the maximum SINR region of the macroBS is suffered the most, and the maximum SINR region for a femtoBS even can surpass the coverage of a macroBS [JAN2013]. This is because of the fact that UEs are comparatively much closer to small cells that a mcroBS that would be installed on a long tower. Hence, it implies that an UE may have good coverage for uplink in one BS and another BS for the downlink. For example, an UE moving towards the microcell edge, may be willing to connect to a femtoBS in the uplink but in the downlink to the macroBS. Further, an UE which is served orthogonally by a BS, e.g., a macroBS does not create any interference in downlink to another macro UE. However, if the same UEs are served by different BSs for the uplink transmissions, they may cause interference to one another, assuming that both are assigned with the same resource for uplink. Hence, classical interference models considering symmetry in uplink and downlink seem to not directly applicable for HetNets as it is the © RONY KUMER SAHA AND CHAODIT ASWAKUL

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case for homogeneous networks where each BS transmit at the same power following a regular cell planning principle. Hence uplink and downlink should be considered as separate networks along with new interference models for HetNets with asymmetry in transmit power and irregularity in deployment. Figure 2.4 shows an example scenario of this new network architecture.

Macro BS link

Backhaul

wn Do Decoupling Uplink and downlink

lin Up

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Figure 2.4 New network architecture based on decoupling uplink and downlink.

Distributed Controlled Network with Decoupling Node and the Baseband Processing Unit In this novel concept [CRA2011], employing virtualization concept on BSs, the baseband signal processing hardware unit (BBU) is decoupled from the node (BS) [FBO2014] and all the BBUs are aggregated centrally to a make virtual BS pool. The centralized BBU, cooperative radio with distributed antenna based RRH and real-time cloud infrastructures radio access network (C-RAN) can address the requirement of 5G networks. With centralized BBU, reduction in site co st, with RRH increase in spectrum efficiency, with real-time cloud infrastructure and BS virtualization, dynamic in resource allocation, reduction in power consumption, and increase in infrastructure utilization can be achieved. C-RAN is an not a replacement of the current networks rather an alternative approach to it and is targeting to most typical HetNets scenario deployed with macrocell , micro cell, picocell, and femtocell [CRA2011]. Based on the layer wise functional splitting between BBU and RRH, full centralization and partial centralization of C-RAN can be realized. In full centralization, baseband, i.e., layer 1, layer 2 and layer 3 are incorporated in BBU, however, in partial virtualization, layer 1 functionality is left with RRH. In either case, a C-RAN consists of three main components: BBU (incorporated with high-performance programmable processor and real-time virtualization technology), distributed RRHs integrated antennas and a high speed low latency optical fiber that connects the RRHs and the BBU. Figure 2.5 shows the C-RAN architectures [CRA2011]. Full centralization gains advantage from capacity extension and easy upgradation but needs high BW fiber as transport media between BBU and RRHs. On the other hand, partial centralization gain advantage from much lower transmission BW but suffers from flexibility in upgrading because of distributed layer 1 functionalities. Because of centralized BBU, joint processing for resource allocation and scheduling, CoMP for interference mitigation can be achieved easily and hence can help improve the capacity of 5G networks.


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Note that because of virtualization feature, and unified BBU, multi-standards can be supported in C-RAN. Through upgrading software or configuration, the same processing board can support multiple standards.

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Figure 2.5 New network architecture based on decoupling baseband processing units and physical nodes (a) full centralization of baseband processing units (b) partial centralization of baseband processing units.

2.5 Summary In this chapter, we have addressed the 5G networks, including the necessity of 5G through reviewing current status of the cellular mobile networks and user demands with a projection towards future requirements to address the demands. We then have mentioned the requirements of 5G networks based on the current researches. Since enormous network capacity is seemed to be the major requirement, we then have identified key enabling technologies that can help boost the 5G network capacity by exploiting the Shannon’s capacity formula in HetNets, includ ing the dense HetNets deployments, the degree of density of small cells deployments, massive MIMO, distributed antenna systems, millimeter wave spectrum, adaptive power control, cooperation communication, and enhanced inter-cell and interference coordination for possible enabling technologies for 5G networks. Since the existing cellular network has not been designed and deployed with HetNets in mind, it is not be capable of addressing many new requirements in HetNets, and hence a rethinking of a new network architecture has shown to be one of the major factors for 5G networks. With this in mind, we then have drawn our attention towards the possible ways © RONY KUMER SAHA AND CHAODIT ASWAKUL

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of how these enabling technologies can be applied through exploiting novel network architectures that h ave been proposed in literature recently as feasible architecture evolution for 5G networks. We then have identified and proposed three major dimensions for network architecture evolution and have addressed two of these three dimensions, namely network nod e and network control. We have mentioned and explained briefly a number of architectural changes based on these two dimensions that imply mainly shifting from the traditional cell centric architecture design to new device centric architectu r e design where decoupling of many existing networking aspects have been identified and detailed to explore in the new architectural paradigm. Alongside, both centralized and distributed network controls are explored. Relevant illustrations and references are included to easily understand these new architectural changes.


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CHAPTER 3 ELEMENTS OF DENSENETS DenseNet is an evolution of the HetNet with extremely large small cells deployed in the coverage of the macrocell. These small cells have low power BSs and cover a certain portion of the macrocell area based on the scenario requirements as will be explained shortly. Small cells such as picocell, femtocell, relay station, and remote radio head (RRH) are mainly considered for LTE-Advanced networks with an expected deployment of device-to-device (D2D) and machine type communications in ad-hoc natured networks as part of the overlaid network in the beyond LTE-Advanced systems. We discuss concisely these elements in this chapter.

3.1 Macrocell A macrocell is usually characterized by high transmit power, wide cell coverage, more user association simultaneously, open access for all users, associating high speed users, etc. In 3GPP LTE, a macroBS is referred to as enhanced nodeB (eNB) and can be either omnidirectional or sectorized (typically 3-sectored, but 6-sectored is also applicable). The number of antennas at a macroBS can be multiple, e.g., in LTE-Advanced, an eNB is standardized with a maximum of eight antennas. Even though, in practice, the radiation pattern does not follow a circular shape (in 2D, e.g., azimuth angle), the hexagonal cell shape is typically used for cell planning because of its area that close almost to the area of a circle. eNBs are connected to each other via X2 backhaul to exchange signaling information (e.g., common control signals, resource allocation decisions, channel state information, etc.) using the C-plane and user data information using the U-plane for relevant cooperation.

3.2 Picocell A picocell is a low transmit power BS with limited cell coverage and limited transmit power as compared to an eNB. A picocell usually serves a few tens of users, and is deployed in hotspot areas such as shopping mall, coffee shop, bus station, office building, etc. where users are more either stationary or low mobility pedestrian. Picocells are usually connected to an eNB with a dedicated X2 backhaul, and they are usually connected in centralized manner. Major purposes of deploying picocells are to improve the local user throughputs within its periphery and also where indoor coverage from the macroBS is poor. Like macroBS, picoBSs are also operators deployed [DLP2011].

3.3 Femtocell A femtocell is also a low power BS with transmit power typically less than picoBS, also called as Home eNB (HeNB) in 3GPP LTE-Advanced, and covers a limited area less than a picocell. Based on the access to a femtocell, femtocells are usually three types: open access, closed subscriber group (CSG), and hybrid access. In open access type, any user can get access to the femtocell; in CSG, only a specific group of users are allowed to get access to the femtocell; and in hybrid access, a portion of the resources are restricted for a particular group of users and the remaining can be accessed by any users. Of these types, usually CSG femtocells are deployed by the subscribers, and open access femtocells are deployed by the operators. Hybrid access femtocells can be deployed either by operators or subscribers, probably driving by the crosstier interference impact. Femtocells are usually deployed in the indoor coverage area to improve the user throughputs as well as the coverage areas where the signal strength from a macroBS is very low or lower than the minimum requirement for establishing and continuing a communication link. Usually serving a dozen of active users in homes or enterprises, femtocells use consumer’s broadband connections as backhaul links such as digital subscriber line (DSL), copper cable or optical fiber © RONY KUMER SAHA AND CHAODIT ASWAKUL

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[DLP2011]. Femtocells play a significant role in offloading data traffic in indoor coverage, which would otherwise be covered by the macroBS and hence help balance the network loads. LTE Release 12 supports the X2 interface between a femtoBS to another femtoBS as well as a femtoBS to a macroBS. Release 12 also supports the provision of X2 gateway (X2 GW) such that femtoBSs can cooperate with each other through the X2 GW, which also connects to a macroBS via X2 interface.

3.4 Remote Radio Head Similar to a macroBS, a Remote Radio Head (RRH) is a high transmit power BS which is usually compact in size and low weight [DLP2011]. RRHs are connected to a macroBS via high speed links such as optical fiber, and all the control and base band signal processing are performed for RRHs at the macroBS. The main purpose of RRHs is to distribute the cell coverage by remote BSs rather than being centralized as in the case of a conventional macroBS such that constraints facin g from, e.g., site acquisitions [DLP2011] or physical limitations can be flexibly tackled by the operators by installing RRHs. This distributed form of antennas, also called as distributed antenna system (DAS) allows having improved link quality, reliability, and coverage because of the more often presence of LOS links and easily overcoming penetration and shadowing loss even with less transmit power compared to the centralized one. Like macroBS, RRHs are also operator deployed.

3.5 Relay Relays are also low power nodes that relay the signals from a macroBS to UEs and vice versa [DLP2011]. A relay node uses wireless backhaul to connect to a macroBS. The link that exists between an UE and the relay node is called an access link, and the link that exist between a macroBS and the relay node is called a backhaul link. Relaying is considered for LTEAdvanced to improve mainly the coverage of high data rates and cell-edge user throughputs and to deploy a network temporarily. Based on the usage spectrum in the access and backhaul links, relays are classified as inband relay and outband relay. When the relay operation is performed at the same frequency on the access link (Uu) and the backhaul link (Un) the relaying is termed as inband relaying; however, if performed at a different frequency, the relaying is termed as outband relaying. Note that the access links from both the macroBS (also termed as donor cell or DeNB that connects communicates with a relay) and the relay node can operate at the same frequency. Relays are classified as transparent if the UE is not aware of whether it is communicating the network with the relay or the donor cell; however, if an UE has the knowledge of which it is communicating with the relay is then termed as nontransparent. A relay can communicate with the donor macroBS as either being a part of the donor cell, in which case it does not possess any separate cell of its own (however, only relay ID it may possess then) or by controlling its cell coverage on its own and possesses a separate physical cell ID and the same radio resource management mechanisms as that of normal eNB. There two types of relay: Type 1 and Type 2. Type 1 relay is characteriz ed by inband, self-controlled cell with a separate physical cell ID [PLA2010]. It can transmits its own synchronization channels and reference symbols. If an UE is associated with only relay, the UE reports controls channels such as channel quality indicator (CQI), acknowledgemen t (ACK), etc. to the relay, and the relay also sends the scheduling information and hybrid automatic repeat request (HARQ) feedback to the UE. Type 1 relay are further categorized into Type 1a if the relay operates outband and Type 1b, if it operates inband with adequate antenna isolation. All others features remain the same in Type 1a and Type 1b as that of Type 1. At least Type 1 and Type 1a relays are included in LTE-Advanced systems. However, a Type 2 relay is characterized by an inband with no physical cell ID [PLA2010]. Hence it cannot create any new cells. This type of relay is transparent to an UE. It can transmit PDSCH, but cannot transmits CRS and PDCCH. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Based on the degree of processing at the relay node, relays can be classified layer wise. A layer 1 as called as amplify-and-forward relay and is the simplest form of all that acts as a repeater and retransmits the signal it receives after amplifying. A simple power amplification functionality is present at the relay station for the amplification purpose. A Layer 1 relay suffers from amplifying the noise along with the desired signal [MIW2010]. However, it is inexpensive and has minimal impact on standard specifications, e.g., 3GPP LTE release 8. A layer 2 relay is a decode-and-forward type which demodulates, decodes and then re-encodes and re-modulates before it resends the received signals to the destination after amplifying it. This relay type advantages from noise elimination, however at the cost of added processing complexity from decoding/demodulation , and the requirement for the radio control functionality be present between the DeNB and the relay station [MIW2010]. Layer 3 relays add few functionalities on top of the layer 2 type relaying are user data regeneration and transmission processing, e.g., ciphering and user data concatenation, segmentation, and reassembly. This relay possesses a unique physical cell ID [MIW2010] such that it is non-transparent to an UE. An UE can then distinguish the a layer 3 relay from a macroBS, and physical layer functionalities such as CQI and HARQ can terminate at the relay station without further transmission to the DeNB. This relay can act as a mini BS, and performs IP packet forwarding in the network layer. A layer 3 relay with inband backhaul has been adopted in 3GPP LTE-Advanced. This relay can also eliminate noise and has small impact on the standard specifications. However, it imposes high complexity and processing delay at the relay station. So far we have discussed is all about fixed relay where relays are a part of the infrastructure network. However, in addition to the fixed relay, mobile relaying can be implemented. There are usually two types of mobile relay systems: moving networks relays and mobile user relays [WAN2010]. In moving networks relays, dedicated relay stations are set up on moving vehicles such as train, bus, etc. to exchange data between macroBSs and the UEs onboard which results in an improved coverage in the vehicle. In mobile user relay system, the distributed UEs can relay information in ad-hoc fashion that can complement the existing cellular networks. Given sufficient infrastructures, theoretical studies have proved that mobile user relays can improve the sum throughputs of users, and hence the system capacity is scaled linearly with the number of users. However, it lacks from constraints such as UE battery power consum ption and also the complicated billing problem s.

3.6 D2D Communications Device-to-Device (D2D) communication is expected to be deployed in the 5G networks. In D2D communications, rather than only macroBSs, others nodes can have the control with the assistance of the network. Specifically, in cell-edge areas where the signal link between an UE to a macroBS is typically weak. However the link between two UEs can be very good such that an UE can act as a transmitter for another UE. In this case, a relay station can hold the responsibility of controlling the link between UEs. A MacroBS carries out all control signaling, e.g., synchronization and provides identity along with security management. Hence, with the assistance of network, a good communication link between UEs can be made possible which would otherwise an UE at the cell-edge communicates with a deeply faded signal link to a macroBS.

3.7 M2M Communications Machine-to-machine (M2M) communications can be defined as the exchange of information among connected intelligent devices, e.g., sensors without the assistance of human intervention. Examples of M2M applications are smart metering, vehicular communications, sensors, etc. [SYE2011]. Figure 3.1 depicts an example scenario of how elements of a DenseNet as explained above are deployed usually, showing explicitly the interface and the backhaul used for control signaling and data information exchange among heterogeneous network elements for cooperation. Further, an example scenario for the future DenseNet for 5G is depicted in Figure © RONY KUMER SAHA AND CHAODIT ASWAKUL

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3.2 where shown is a highly overlapped and an extremely dense small cells in the coverage of a macrocell that are expected to be deployed, employing advanced technologies such as massive MIMO, orthogonal spectrum allocation, network cooperation (CoMP), and advanced interference mitigation and alignment among cells. Table 3.1 [DLP2011] [GHO2010] shows a comparative framework of DenseNet elements discussed so far in this section.

Mobile network relay

ss k n l e l i n U fa c e i re u l er W kh a Int c ba

Mobile user relay

Dir

Direc

ec t l ink W b a i re ckh l e s au s l li nk

t link

Macro BS

Direct link Uu Interface ace erf l Int kh a u X2 r bac Fibe

ac

D2D link

Dir

e

ec

t lin

k

l

rf

kh

D2D link

Uu Inter face s link Acce s

Direct link

Wireless Backhaul link

e

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FemtoBS

er Int

ac

Un

X2

te In

RRH

au

PicoBS

ce terfa X2 In l kh a u r bac Fibe

Fixed relay

D2D link

Figure 3.1: An example scenario for elements in DenseNet.


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D2D link

femtoBS

PicoBS

Relay

RRH

Figure 3.2: An example scenario for 5G DenseNet. Table 3.1: A comparative framework of DenseNet elements. DenseNet Elements

Transmit power

Cell coverage

RAN Backhaul connection

RAN backhaul interface

macrocell

46 dBm

Few km

X2

Picocell

23-30 dBm

Less than 300 m

Optical fiber, LOS microwave Optical fiber, LOS microwave

Femtocell

Less than 23 dBm

Less than 50 m

DSL, copper cable

Relay

30 dBm

300 m

RRH/ DAS

46 dBm

Few km

Specifications Attributes Utilization

Access

High mobility UE associations, wide coverage, etc. Capacity improvement, indoor and outdoor coverage provision, macro data traffic offloading, etc.

Open

X2 (LTE rel. 12)

Capacity improvement in indoor environment, macro data traffic offloading, etc.

Wireless

X2 (with Donor macroB S)

Optical fiber

X2 (with macroB S only)

Coverage extension to places where wired backhaul is not feasible, signal diversity, setting communications in natural disaster, cell-edge user throughputs improvement, etc. Cooperative gain, flexibility in site acquisition, wide area coverage, handoff reduction

Open, closed, and hybrid Open

X2


Open

Open

Deployment environment and planning

User mobility

Outdoor and Planned Indoor (mainly), outdoor, and Planned Indoor and unplanned

Stationary to high mobility

Number of Tx/Rx antennas (for LTE-A) 2/2 or 4/4

Stationary users and pedestrian users (mainly)

2/2 or 4/4

Stationary users and pedestrian users (mainly)

2/2 or 4/4

Outdoor, natural disaster, and Planned

Low mobility users for fixed relay, medium mobility users for mobile relay

2/2 or 4/4

Outdoor and Planned

Stationary to high mobility users

2/2 or 4/4

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3.8 Summary In this chapter, we have discussed the major elements of DenseNets with identifying major characteristics and functionalities, and wherever appropriate, relevant illustrations. The small cells that are deployed in the coverage of a macrocell can be either the operator or the user deployed and can share the frequency among them and (or) with the macrocell. Cooperative elements such as relay has been detailed more elaborately as a continuation to towards cooperative communication that will be addressed in the next chapter. A comprehensive comparison of the DenseNets elements has been provided and with an example scenario, how these elements integrated in the cellular network have been illustrated. In addition an example 5G DenseNet deployment scenario has also been illustrated for visual comprehension where cells are deployed densely overlapping one another with appropriate cooperation between them.


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CHAPTER 4 COOPERATION IN DENSENETS 4.1 Introduction Cooperation can be defined as the process of working together [WZH2012] which in cellular wireless networking aspects, a number of BSs and UEs cooperate each other to address a common network goal such as channel diversification , resource allocation, interference mitigation to name a few. The perspective of Cooperation is broad and in this chapter, we particularly emphasis on those perspectives of cooperatio n that are highly relevant to the practices on the current mobile networks, specifically 3GPP LTE w ith a projection towards the future one such as 5G network. Following Bernard Bull, even though the terms networking, coordinating, cooperation , and collaboration in some scenarios of networks have been used interchangeably, they do however differ from one another in terms of the degree of interaction, purpose, and benefit achieved mutually. Let’s consider an example scenario where two BSs (BS_X and BS_Y) in cellular communications interact one another. If these nodes simply exchange information (e.g., CSI) for mutual benefits, we can term it networking; however, if these nodes along with exchanging information, alter few of their activities (e.g., when the BS_X schedules resource block A at any time, the BS_Y does not) is termed as coordination. If however, on top this mutual benefits, they involve with attaining any common purposes (e.g., network interference mitigation through resource coordination), it is termed as cooperation; and when these nodes also share some resources of their own to the other, for example BS_Y shares few of its own resource blocks, subframes, etc. (that have already been allocated by the network scheduler among these nodes) to improve a cell edge-UE’s throughput of BS_X is termed as collaboration. It is interesting to know that all sorts of these mutual benefits with or no common purposes have already been existed in the current cellular networks, as we will see shortly in what follows.

4.2 Cooperative Communication The concept of cooperative communication addressed first by [ECM1971] proposed a three-terminal relay channels with a derivation of upper and lower limits of capacity. Later on the authors in [TMC1979] investigated the capacity of cooperative relay channel and sets a theoretical basis for the research on cooperative communications. As the mobile networks get complex with time because of addressing the high demand of users, the management of the networks has become a crucial issue. Several solutions have also been proposed and developed alongside from both the network mechanism and the new node deployment. Currently the LTE release 10 (LTE-Advanced) has standardized the cooperative communications as one of the advanced technologies to address many crucial issues such as interference, capacity, diversity, cell-edge user throughput, etc. by including a new node called relay stations (RSs) and coordinated multipoint (CoMP) communication. As discussed in section I, RSs are used to allowing communication to a destination node such as an UE by relaying the signal to the network and vice versa. The RSs coordinate with the network via backhaul links. In CoMP, a group of network nodes coordinate among each other and the group is termed as cooperating set which can be control static or dynamic fashion based on the UE mobility and other aspects. This type of cooperation is termed as node cooperative systems.

4.2.1 Relay Cooperative Systems The relay node use three cooperative protocols such as amplify-and-forward, decode-and-forward, and compress-andforward. The first two protocols have been defined in section I. while considering compress -and-forward protocol, the relay first receive the signal, maps it to another signal in a a reduced signal space, re-encodes and then forwards the compressed signal to the destination node. If the backhaul link is poor, amplify-and-forward and compress-and forward protocols are favorable, however for good link condition at the relay backhaul, decode-and-forward protocol is more advantages [QLI2012]. Both type 1 and type 2 relays can cooperate with the network macroBS. UE can be associated with either macroBS or RS, however, in type 2 relay cooperation, an UE is associated only with the macroBS. Since relays are deployed in the coverage of a macrocell, relay cooperative schemes are also called intra-cell CoMP where relay node and the macroBS cooperate each other. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Type 1 relay cooperative scheme completes four transmission frames [QLI2012] as follows. In the first frame, DeNB sends the data and control information to RS via PDSCH and PDCCH channels respectively. In the second frame, RS takes decision on whether the UE is to be served cooperatively with the DeNB or not. If the cooperation is chosen by RS, then the relay sends the corresponding scheduling and control information via backhaul link to DeNB. In third frame both DeNB and RS send data to the UEs following the scheduling information sent in the second frame. Finally in the fourth frame, an UE sends either ACK or NACK information back to its associated RS. In Type 2 relay cooperation, communications takes place in five steps [QLI2012]. In the first frame, DeNB transmits the scheduling and data information to an UE while RS simply monitors it. In the second frame, the UE sends ACK/NACK message to its associated macroBS (i.e., DeNB since we implicitly consider that macroBS has the relay link existed) and this time also, RS monitors the transmission. In third frame, if NACK is received in the second frame, DeNB sends the scheduling information to RS for arranging retransmission. In the fourth frame, DeNB sends scheduling information the UE and RS sends the data information to the UE. To improve the link throughput at the UE, DeNB can also choose to transmit the same information to the UE at the cost of additional use of resources. The UE now can decode the data information using signal received on the first frame by DeNB and in the fourth frame by RS. In the fifth frame the UE sends ACK/NACK message to its associated macroBS. Figure 4.1 [QLI2012] illustrates the intra-cell CoMP using relay cooperative communications.

Scheme 1

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SC H)

/ CK g) 2 (A nitorin me o Fra K M C ) CH NA DS 4 (P me Fra

UE

RS (Type 2)

UE

Figure 4.1 Relay cooperative intra-cell CoMP.

4.2.2 Node Cooperative Systems Cooperation in node cooperative systems can be held by either joint processing or coordinating strategies among the communication nodes. In joint processing, data among all cooperating nodes are first exchanged via means such as backhau l link connecting one another and transmission or reception of data takes place jointly from all the nodes at a time to improve mainly the user throughput. All cooperating nodes connected to each oth er via high speed backhaul links form a distributed antenna system (DAS) which can easily take advantage of spatial diversity and hence results in overall network capacity gain. In coordinated node cooperation, nodes coordinate strategies, e.g., resource allocation, beamforming pattern, etc. each other via backhaul links in order to mainly reduce the interference from adjacent nodes. Control information such as CSI is shared for coordination among all cooperating nodes. In LTE-Advanced systems node cooperative communication is termed as coordinated multipoint (CoMP) communications, and both joint processing and coordinated strategy based © RONY KUMER SAHA AND CHAODIT ASWAKUL

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cooperation are considered which are referred to as respectively joint transmission/reception (JT/JR) CoMP and coordinated scheduling/coordinated beamforming (CS/CB) CoMP. CoMP is considered for LTE Release 11 with a purpose to improve the coverage of high data rates, the cell -ed ge throughput, along with increasing the overall system throughput [ 819TR2013]. Unless specified, we consider the downlink in what follows.

4.3 CoMP Transmission and Reception Schemes There are mainly two categories for CoMP transmission and reception (Tx/Rx): joint processing (JP) and coordinated scheduling/coordinated beamforming (CB). JP is further categorized into two: joint transmission (JT) and dynamic point selection (DPS). Irrespective of the CoMP schemes, the control signals, e.g., PDCCH are only transmitted from the serving cell (the cell where the current physical location of an UE exists. In JT, all points or partly in the cooperating set associated with a UE specific demod ulation reference signal (US-RS) among them transmit user data simultaneously to a UE coherently or non-coherently in a time-frequency resource to improve the received signal quality and/or data throughput. However, in DPS/muting, user data is transmitted from only one point within the cooperating set in a time frequency resource while all other points in the cooperating set is muted, i.e. not transmitted even though user data is available at all points in the set. The transmitting or muting a point may change simultaneously from one subframe to another following specific scheduling strategy such as the minimum path loss between the point and the UE [MSA2010]. Hence, a cell-edge user does not suffer interference from other cell and maximum received signal quality can be achieved at the UE. DPS may be combined with JT such that a subset of the cooperating set can transmit simultaneously in a time-frequency resource. In CS/CB, user data is available in only the serving point, however, the scheduling or the beamforming decisions are taken with coordinating all other nodes in the cooperating set. The transmission points are changed semi-statically. With a combination of JP and CS/CB that results in a hybrid scheme may be possible where user data is available only in a subset of points in the cooperating set and however, user scheduling and beamforming decisions are made with coordination among all points in the cooperating set. In this way, a few points can involve in joint transmission simultaneously while other points can cooperate for CS/CB in a time-frequency resource. All these schemes are shown in Figure 4.2.

Us er d a

ta

UE

CS/CB CoMP

RRH ata User d

UE

Side loop User data

UE

UE

Backhaul

RRH

Us er d a

ta

JT CoMP

McroBS

Mu ting u tran data ser smi ssio n

ta

Interference coordination

Us er d a

Beamforming

DPS CoMP

RRH

Figure 4.2 CoMP schemes in heterogeneous LTE-Advanced networks. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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4.3.1 CoMP Cooperating Set A CoMP cooperating set is defined as a set of geographically separated points or nodes participate directly or indirectly for data transmission to a UE in a time-frequency resource. If points in a set are actively involve is data transmission, points are called participated directly, and however, if they do not involve in data transmission but do involve in making decisions on user scheduling and beamforming in a time-frequency resource are called participated indirectly. Note that it is not a necessity as we have already discussed, all points in a cooperating set do act as data transmission points (such as in JT), and usually a subset (such hybrid JT and CS/CB) or a single point (CS/CB and DPS) can transmit only data while others in the set help coordinate the process. Cooperating sets can be transparent or non-transparent to an UE. If the knowledge of the set is known to an UE, the set is referred to as transparent cooperating set which depends on the cooperation schemes. A cooperating set also defines the coordination area for an UE.

4.3.2 CoMP Scenarios Four scenarios are considered for CoMP in release 11 for both uplink and downlink. Scenarios 1 and 2 are for homogeneous networks and scenarios 3 and 4 are for heterogeneous networks. As shown in Figure 4.3 (a), in scenario 1, homogeneous network with intra-site CoMP is considered. With homogenous we define a network deployed with the same cell size and with the same transmit power. However, in scenario 2 homogeneous network with high transmit power RRHs is considered as shown in Figure 4.3 (b). Due to larger coordination coverage as compared to scenario 1, more gain from coordination can be achieved in scenario 2 [SSU2013]. Heterogeneous network with low power RRHs with different physical cell IDs (PCIs) from that of a microcell are deployed in the coverage of a macrocell in scenario 3 as illustrated in Figure 4.3 (c). Different from scenario 3 in scenario 4 is that the RRHs have the same cell IDS as that of the macrocell as shown in Figure 4.3 (d) [819TR2013]. Hence, no new cell is formed that requires separate control channel design and mobility management as in the case of scenario 3 and hence simplifies the control plane aspects and reduces the cooperation overhead s in the backhaul originate mainly from the downlink interference experienced by a macro UE near to a RRH coverage-edge by the RRH and a RRH-served UE by the macrocell. However, because of the same network topology in scenarios 3 and 4, the data plane aspects such as data transmission do not have any changes in both scenarios [SSU2013].

4.3.3 CoMP Deployments Two deployment cases for intra-eNB with ideal backhaul between transmission points are considered. For scenarios 1 and 2 macro or high power RRH plus macro or high power RRH CoMP whereas for scenarios 3 and 4, macro node plus low power RRH CoMP are considered as transmission points. Since scenario 1 and 2 employs homogeneous network, and assuming CoMP works for cell edge user performance improvement, a small propagation difference between coordinating nodes would be occurred. However, since scenarios 3 and 4 uses low power RRH that is deplo yed in the coverage of a macro node a large difference in propagation delay can be occurred because of the distant between the UE and the nodes (macro and RRH) when considering that the RRH is deployed at the cell edge of a macro node. A timing offset at t he UE can be considered, defined as the observed timing of transmission points that are transmitting PDSCH with respect to the serving cell. Specifically, Timing offset = Difference of BS timing alignment errors+ difference of propagation delays

(4.1)

The propagation delay can be found from the cell radius over the speed of light. In release 11, a timing offset of within the range -0.5 to 2 microseconds is defined as UE performance requirements.

4.3.4 CoMP Feedback Mechanisms There are mainly three categories of channel state information (CSI) feedbacks to the points from the UEs, including explicit channel state or statistical information feedback, implicit channel state information feedback, and UE transmission of sounding reference signal (SRS).


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Explicit CSI feedback is defined as a feedback which carries actual channel information as observed by the receiver, with no processing signal. Whereas, implicit CSI feedback includes a processed channel information such as channel quality indicator (CQI), precoding matrix indicator (PMI), or Rank indicator (RI) which are derived from some hypothesis of different transmission or reception processing, rather than the actual channel information. Further, exploiting channel reciprocity, it is possible to estimate CSI at a point from SRS transmission of an UE. Combining all these three and a subset of these can be used for periodic and aperiodic feedback reports.

3-sectored macroBS

RRH RRH

Sector 3

RRH

Sector 2

Sector 1

macroBS Inter-sectored/Intra-site CoMP Coordination area

RRH

RRH RRH Coordination area Backhaul links

(a)

With PCI

With PCI

RRH

macroBS With PCI

With PCI

RRH

RRH

Without PCI

backhaul links

(c)

Without PCI

RRH

Macrocelledge RRH

(b)

macroBS Without PCI

Without PCI

RRH

RRH

Macrocelledge RRH

backhaul links

(d)

Figure 4.3 (a) Scenario 1 - Homogeneous network with intra-site CoMP. (b) Scenario 2 - Homogeneous network with high Tx power RRHs. (c) Scenarios 3 - Network with low power RRHs within the macrocell coverage (RRH with own PCI). (d) Scenarios 4 - Network with low power RRHs within the macrocell coverage (RRH with no separate PCI). Any of these CSI feedback mechanisms may serve more than one CoMP categories or different feedback mechanism may be applicable for different CoMP categories. Usually for CS/CB CoMP category, CSI feedbacks are needed from multiple points, however, inter-point phase information is not necessary and so is the requirements for the DPS CoMP with a provision for some additional CQI report targeting other points. However, for JT CoMP, CSI feedbacks are needed from multiple points along with inter-point phase information in case of coherent transmission. Additional information such as inter-point amplitude information may be needed as well. The UE feedback reports may contain CSI measurement relative to one or multiple points and the points in the cooperating set that receive UE reports is network implementation specific, i.e., may vary from one network to another. Also exchange of feedback report among cooperating points is subject to the backhaul capacity. An explicit CoMP feedback report consists of two parts: a channel part and a noise-and interference part. Channel information may contain full channel matrix or main Eigen components of it and interference part may contain total received interference power or total receive signal covariance matrix. Implicit feedback report may contain one or a combination of the hypotheses such as single vs. multi user MIMO, single point vs. coordinated transmission, transmit precoder (weights), CQI, etc.

4.3.5 Backhauling and Overhead for CoMP The points in all four CoMP scenarios may be connected to the same eNB (intra-cell CoMP) or different eNBs (inter-cell CoMP) for scenarios 2, 3, and 4 point-to-point fiber can be used. However, higher latency and limited capacity backhau l may be used to scenarios 2 and 3. Both inband and outband relays or a combination of them can be used as backhaul link. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Backhaul links between eNBs usually exist and depending on backhaul technology, asymmetric capacity and latency can be possible between directions of information flow to and from a point connecting the two points. Because of cooperation among multiple points, a large amount of overhead exist from information exchange among points. Specifically, in release 11, compared to release 10, in the downlink, an increase in overhead due to CSI -RS and/or muting patterns is expected. In the uplink, overhead increase from CSI and SRS measu rements for multiple points is also expected . This may result in some performance degradation in terms of capacity, latency, etc. for example because of more usage of resources for overhead. A proper scheduling of resources such as scheduling restrictions may be applied to improve the performance.

4.3.6 Uplink CoMP Most principles that have been described for downlink CoMP transmission are also applicable for uplink CoMP reception , with a basic difference is that in uplink, the transmitter is the UE and the receivers are the points in the CoMP cooperatin g set. Thus, we will not discuss much on the uplink CoMP reception. Like downlink CoMP transmission, uplink CoMP reception schemes include mainly joint reception (JR) and coordinated scheduling and beamforming (CS/CB). When PUSCHs are received jointly at geographically separated multiple points (entire or a part of CoMP cooperating set) from by the UE at a time called is called JR CoMP in order to improve the received signal quality. In CS/CB, the user scheduling and the precoding selection decisions are made with coordination among points in the cooperating set and like CoMP transmission scheme for CS/CB, data is intended only for a single point.

4.4 Cooperative Interference Management 4.4.1 Interference in DenseNets Interference in dense HetNets is one of the major bottleneck that seems to more severe than tradition homogeneous networks. Major sources of interference in HetNets are unplanned deployment, restricted femtocell access, transmit power difference among points, and new techniques such as cell range expansion [DLP2011]. Small cells such as femtocells are not deployed following any predefined plans. These are partly deployed by the users in an unplanned fashion with almost no operator consciousness about the density, location, access types of femtocells. Hence, in such an unplanned deployment scenario, it is difficult to manage interference. Rather than centralized, interferen ce management in distributed fashion locally at the cell areas are expected to be more effective based on local interferen ce scenarios. When femtocells are configured as CSG, the interference intensity become the most of all other access types of femtocells. More specifically, a macro UE close to the coverage of a CSG femtoBS interfered highly in its downlink reception by the CSG femtoBS (Figure 4.4 (a)) since the macro UE is not allowed to get access to the femtoBS. Similarly in its uplink transmission of macro UE to a far distant macroBS (considering most femtoBSs deployed near the cell-edge areas, a typical scenario in HetNet) transmits at high power to compensate the far distant dependent pathloss and hence, causes interference by jamming the uplink transmission from femto UEs to the femtoBS and causes significant cross -tier interference. Note that the cross-tier interference implicitly assumes the both macroBS and femtoBS work on the same frequency to reuse the same spectrum as many times as possible by maintaining minimum geographical distant separation between femtoBSs. When tiers operate on different frequencies, the interference is avoided but at the cost of more usage of spectrum resources and hence, results in a reduction of area spectral efficiency (bps/Hz per unit area). Further unevenness in transmit power of different points in HetNet is another issue for the cause interference. Unlike CSG femtoBS, other low power points such as picoBSs is open-access, operator deployed BSs. Since an UE usually gets connected to a BS with the highest downlink signal strength such as RSRP in its neighbor BSs that it can detect, an UE prefers to get connected with the macroBS because of higher transmit power than a picoBS. This creates a phenomenon called imbalance in load distribution between cells and macroBS is likely to get overloaded almost always, even though there are picoBSs around an UE at distant much shorter than the distant from the macroBS it gets already connected with. Moreover, UEs in the near coverage of a picoBS interfered highly in the uplink from the picoBS ( Figure 4.4 (b)) as explained for the case of CSG femtoBS. If UEs near a picoBS were getting connected to it, UEs may transmit in the uplink at reduced transmit power because of shorter distance dependent pathloss as compared to the macroBS. This would help UEs not be sufferers from interference in the uplink since UEs are now communicatin g with the picoBS and also ensure the load balance by offloading traffics from the macroBS. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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The offloading problem and the interference in the uplink for a macro UE can be addressed by expanding the actual picocell area with adding an offset to the RSRP of a picoBS such that the effective cell area of the picoBS is expanded, also this new cell selection criteria is also referred to as cell range expansion (CRE) [NTT2010]. With CRE picoBS, an UE receives higher RSRP from the picoBS than without employing CRE to the picoBS and hence, more UEs are likely to get connected to the picoBS and hence resulting more macro UEs offloading to the picoBS and also less uplink interferen ce. However, pico UEs in the expanded picoBS cell area are likely to suffer from the macroBS in the downlink (Figure 4.4 (c)). This can be overcome with resource scheduling coordination between macroBS and picoBS via backhaul signaling such as orthogonal RBs are scheduled near the victim pico UEs such that the RBs scheduled for pico UEs are reused in other parts of the macro areas to macro UEs with sufficient distant separation from the victim pico UEs.

nli nk ow

link D ow n signal

D

MacroBS

ence rfer e t n i ence link rfer e t Up n i ink wnl Do

Do

wnl ink

inte r

Pico UE

(b) PicoBS

Pico UE

link D ow n signal

ink wnl Do gnal si

fere n

Macro UE

ce

D ow sig nlin na k l

Do inte wnlink rfer ence

sig na

l

Macrocell edge

Femto UE

Macro UE

(a) CSG FemtoBS Cell range expansion (CRE)

(c)

CRE employed PicoBS

Figure 4.4 Interference in HetNets.

4.4.2 Inter-Cell RRM for Interference Management Using CoMP, tight inter-cell radio resource management (IC-RRM) for orthogonal resource assignment such that interference can be managed within limits can be achieved. Two approaches [MSA2010] centralized and autonomous ICRRMs can be considered. In centralized IC-RRM, a group of RRHs are connected by high speed backhaul links (e.g., fiber) to a centralized BS (also called eNB). All the baseband signal processing is performed at the eNB. Radio resources are scheduled at a single point and scheduling decisions are coordinated among all RRHs, the interference is mitigated considerably by orthogonal resource assignment. This centralized IC-RRM approach is beneficial to heterogeneous networks where independent link connections between uplink and downlink to different nodes can be possible because of the centralized processing at the eNB. For example, the uplink can be connected to a small cell, e.g., picocell and the downlink is connected to a RRH of the eNB of an UE. Hence, this in turn mitigates the interference at an UE along with providing necessary UE throughputs at uplink and downlink based on necessity. Coordination with the picocell can be performed via the X2 backhaul with the eNB.


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In autonomous IC-RRM, eNBs are connected to each other via X2 interface and relevant information for resource scheduling are exchanged between them such that the interference at an UE, particularly a cell-edge user is minimized. This approach is particularly important for a macro UE near the coverage of a CSG femtoBS where a macro UE receives weak macroBS signal strength and is interfered significantly in the downlink from the nearby CSG femtoBS because of no access to the femtoBS. The ICIC gain from this approach employing X2 interface between eNB and femtoBS has been reported in the literature. Figure 4.5 shows a sample network architecture for IC-RRM. Centralized IC-RRM Autonomous IC-RRM

X2 Interface

RRH

In X2

te RRH rface

eNodeB

eNodeB X2 Interface

ce rfa te In

RRH

X2

e terfac X2 In

X2 Interface

Pico X2 Interface BS

RRH RRH

Figure 4.5: Network architecture for IC-RRM. For coordination among points where X2 interface is present, a number of information messages are exchanged among them to facilitate the coordination process. The inter-cell interference coordination (ICIC) messages defined for releases 8 are relative narrowband transmit power (RNTP), overload indicator (OI), and high interference indicator (HII). An RNTP is used to indicating by a point in the cooperating set to inform others whether or not to set the downlink transmit power level below a certain threshold for specified RBs. For uplink transmission, OI in the form of average interferen ceplus-thermal-noise power measurements for each RB is exchanged among points. HII is transmitted by a point to inform its neighbors that one of its cell-edge UE will be scheduled in the near future for uplink transmission such that its neighbor pints can withhold scheduling their cell-edge UEs in those specified RBs. In 3GPP release 10, frequency domain, time-domain and power control ICIC techniques are defined to better address the interference also aggregately called as enhanced ICIC (eICIC) [DLP2011]. In time-domain eICIC, the victim UE is scheduled in time domain while the interference from other points are reduced. Use of almost blank subframe (ABS) is one way to address time domain eICIC where the victim UE is scheduled in the ABSs [DLP2011]. For example, if a macro UE is near the coverage of a CSG femtoBS, the macro UE is interfered significantly from the femtoBS. In this case ABSs can be applied at the femtoBS such that the macro UE will be scheduled only in the ABSs. In ABS, data and control signal are not scheduled, however only reference signal is scheduled as shown in Figure 4.6 (a). ABS can also be applied for picocells with cell range extended, in which case, ABSs are applied at the macroBS while the pico UE in the extended coverage of picocells is scheduled in the ABSs only to reduce the interference from the macroBS (Figure 4.6 (b)). Note that cell range extension is used to extend the coverage area of picocells because of their low transmit power compared to the macroBS such that more macro UEs can be offloaded to the picocells with setting an offset to the reference signal reference power (RSRP) of picocells.


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The other approach to address eICIC is to apply shifting to the subframe boundary by a number of OFDM symbols of one point with respect to another. For example, the subframe boundary of a femtoBS is shifted by a number of OFDM symbols with respect to the macroBS (Figure 4.7) such that the control channels of both BSs are not overlapped each other. However, the control channels of the macro UE (near to the femtoBS) are still interfered by the data channels of the femto UE. To overcome this problem, one approach is to mute those overlapped OFDM symbols of femtoBS with the control channels of the victim macro UE, and the other approach is to configure the subframes of the femtoBS as ABSs as given in Figure 4.6 (a) that overlap the control channels of the macro UE. The term almost blank subframes comes from the fact that the transmission power in ABSs is not completely zero rather it is reduced by some dB, let’s say Y dB in comparison with the normal transmission (Figure 4.8) [BSO2013]. This is because to provide support for the legacy UEs, the interferer BSs still needs to transmit some essential signals such as common reference signals (CRSs), physical broadcast channels (PBCH), and primary and secondary synchronization signals (PSS and SSS). There are a number of ABS patterns as identified by the 3GPP [R412011] such as for FDD, 1/8, 2/8, 3/8, and 3/20 ABS patterns are considered. Here 1/8 means 1 ABS of every 8 subframes (10000000, …, 1000), and so is for 3/8, ABS sequence is (11100000, …, 1110). Note that in the ABS patterns, 1 represents the almost blank subframe, and 0 represents normal subframes in time-domain. Hence the performance of eICIC depends on many factors [YWK2012] such as ABS muting patterns, UE traffic distributions, and interfered BS settings. In frequency domain eICIC technique, by orthogonal scheduling of control channels between cells in reduced bandwidth, interference can be mitigated. Coordinated resource scheduling decisions are exchanged among BSs. Along with static orthogonalization, dynamic frequency-domain orthogonalization can be performed with the detection of victim UE. The interfered UE can be detected at the BS based on UE specific measurement reports. The victim UE’s BS then informed the interferer BS through backhaul signaling. The victim UE can also be detected by the interferer BS and can coordinate its scheduling decisions with the victim UE’s BS [DLP2011]. In power control eICIC technique, different power control technique can be applied to the small cells such as femtocell which has been discussed heavily in 3GPP to handle dominant interferers [DLP2011]. However, with a reduction in transmit power level at the femtoBS, will eventually reduce the throughput of femto UEs at the gain of macro UE interferen ce reduction. Some trade-offs can be assumed based on the scenarios such that the optimal power control at the small cells can be achieved that will contribute to the overall system capacity improvement.

4.5 Cooperative Carrier Aggregation and Scheduling In LTE-Advanced systems, carrier aggregation (CA) is one of the important tools for addressing high data rates. For LTEadvanced, a maximum number of 5 spectrum chunks called component carriers (CCs) each with 20MHz can be aggregated from a single or more than one bands (carriers) to achieve a maximum system bandwidth of 100MHz and t he process of aggregation is referred to as CA. When CCs are aggregated from the band contiguous to one another the CA is then termed as contiguous CA; if considered from different bands such 1.8GHz, 2.1GHz, and 2.6GHz is termed as noncontiguous CA. an LTE-advanced UE is assigned with a CC is called its primary cell (PCell) which acts as an ancho r for the UE so that all basic functionalities are performed at the PCell [MIW2010]. If an UE uses more than one CC in the downlink, the additional CCs are called secondary cells (SCells) for the UE. In order to reduce the UE power consumption, if an UE does not involve data transmission with SCells for a long time, the SCells can be deactivated by the network by simply setting a deactivation timer without explicit feedback from the UE. Hence, if the UE wants to connect with SCells after deactivation, SCells have to be activated first since SCells are by default configured as de-activated and so is not the case for PCell – always activated and not subject to de-activation procedure. When small cells layer in HetNets use different frequency from that of the macroBS, the inter-site (between a macroBS and small cell BSs) interference naturally overcomes from orthogonal frequency use in macrocell and small cell tiers. However, CA between the tiers can be exploited so that an UE can be assigned with more bandwidth by assigning to more than one CC to improve the user data rate. If we consider small cells as RRHs, they are connected to the macroBS with high speed backhaul link and the scheduler of RRHs are implemented at the macroBS. Note that the schedulers of other small cells such as relay, femtocells, and picocells also have their schedulers at the macroBS. So, the discussion is equally applicable for other small cells as well. Consider that a macroBS is operating at carrier f 1, and overlaid RRHs are operating at carrier f 2. If an UE is configured with the macroBS as PCell and RRHs as SCells, the UE can opportunistically connect


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Odd subframe = 1 ms 7 symbols (normal CRC)

Even subframe = 1 ms 1 symbol time

1 time slot = 0.5 ms

1 RB = 180 kHz

... Reference subcarrier Control subcarrier Subcarrier 15 kHz

MacroBS

Data subcarrier Almost blank subcarrier

... FemtoBS (CSG) (a)

... Reference subcarrier Control subcarrier

MacroBS

Data subcarrier Almost blank subcarrier

... PicoBS (CRE employed) (b) Figure 4.6 ABS based time-domain eICIC (a) between macroBS and femtoBS (b) between macroBS and CRE based PicoBS. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Interfered macro UE control channels

Odd subframe = 1 ms 7 symbols (normal CRC)

Even subframe = 1 ms 1 symbol time

1 time slot = 0.5 ms

1 RB = 180 kHz

... Reference subcarrier Control subcarrier Subcarrier 15 kHz

Shifted by 3 symbol durations

FemtoBS data channel interferers

Data subcarrier

MacroBS

Almost blank subcarrier

... FemtoBS (CSG) Figure 4.7: FemtoBS subframes are shifted by 3 symbol durations with respect to the macro UE’s.

Interferer BS Transmit Power

Interfered UE is scheduled

Normal power level (dB)

ABS

Y dB

CRS power Subframe Figure 4.8: Transmit power vs. subframe in ABS. with an RRH based on the RSRQ measurement such that the interference results in because of the use of additional f2, which may be coupled with other surrounding cells or UEs, is within an acceptable level. The above technique is similar to autonomous CC selection (ACCS) technique [LGA2009]. In ACCS, a BS can always have at least one CC with full coverage. With an increase in traffic of the BS, additional CCs can be allocated to increase its capacity, provided that the use of additional CCs does not create excessive interference to its surrounding cells. However, BSs are assumed to operate at the same transmit power spectral densities [BSO2013]. The ACCS is a fully distributed and dynamic interference technique intended to allow assigning additional CCs to a BS while taking the interference in the surrounding BSs into account. The ACCS technique can be suitable for femtocells which are expected to be deployed densely and unplanned fashion in HetNets. In ACCS technique, background interference matrices (BIMs) are built locally at the femtoBSs based on downlink RSRP measurements. BIMs give indications of carrier-to-interferen ces (C/I) ratios experienced whenever two © RONY KUMER SAHA AND CHAODIT ASWAKUL

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femtoBSs use the same CC. Hence, by exchanging the RSRP measurements among all femtoBSs, a femoBS can learn the interference about its surrounding cells. Based on the results shown by [KIP2011], where authors considered three CCs to assign to femtoBSs taking into account of three scenarios: reuse 1, reuse 3 and ACCS of the CCs, it can be concluded that the ACCS technique improves the performances in terms of both 5-percentile (outage) data rates as well as average data rates and can surpass even the universal reuse (i.e., all cells can use all CCs) because of its robust trade-off between the bandwidth assignment and the signal-to-interference –plus-noise (SINR) ratio. This kind if CA is called as intra-site CA [BSO2013] because of the same cell type used for CA. Consider the Proportional Fair (PF) schedulers for the CA at the macroBS. Since the macroBS has all the information about the UEs, the cross-carrier scheduling between the macrocell and small cells (i.e., RRHs for inter-site CA) or between small cells (i.e., femtoBSs for intra-site CA) can be performed simply by updating the previous average throughputs of an UE over all the cells where the UE has been scheduled, in contrast to a single cell as in the case of a generic PF scheduler. The additional task that needs to be performed is to exchange the user past average throughputs between the schedulers for the macrocell and small cells (for inter-site CA) and between small cells schedulers each operate for a different CC. Note that for intra-site CA, there is no need for exchanging inform ation if only one global scheduler is considered for all the CCs. We consider implicitly that different cell types at different CCs have individual scheduler (i.e., one scheduler for each carrier for each cell type) at the macroBS and, the schedulers can operate either individually or jointly (as explained for the inter-site CA) for this discussion. Also for the intra-site CA, we consider only small cells, no macrocell is considered . Hence, for the joint PF scheduling, the performance metric of the PF scheduler is to be modified from the equation (4.2) for a single carrier scenario to equation (4.3) for the joint PF cross-carriers scenario as follows [YWA2010].

Rk ,i , j (4.2) X k ,i , j  ~ Rk , i X k ,i , j 

Rk ,i , j N

(4.3)

~

R i 1

k ,i

X k ,i , j is the performance matric for UE k on the ith CC at the jth RB

Rk ,i , j is the estimated throughput for UE k on the ith CC at the jth RB

~ Rk ,i is the past average throughput of the UE k on CC i.

N is the total number of CCs (e.g., for the above examples, N is equal to 2 for inter-site CA and 3 for intra-site CA)

4.6 Backhaul Networks 4.6.1 Backhaul Networks and Deployment Choices The future mobile networks will be denser than the current HetNets practice in LTE -advanced. Hence, consequently the network management will also more difficult than today’s networks where tens of thousands of small cells with different transmit power, cell coverage, QoS requirement, etc. will be deployed at the same frequency or different frequen cy overlapping area of one another. In such complex HetNets, the tight integration and high level of cooperation between cells are crucial to address several significant and unavoidable issues such as interference and load balancing. The backhaul networks will play a significant role on the overall performance gain in terms end -to-end of uniform quality of experience (QoE), cost effectiveness, etc. backhaul networks connect each BSs in HetNets, provide a channel to communicate and cooperate one BS to another to address a common network goal. Moreover. Backhaul networks take a considerable share of the total cost of ownership of the network. Hence, backhaul solutions should be cost -effective, easy to install, highly scalable and flexible, and not be a barrier to the performance of the HetNets [ERA2014]. However, to maximize the use of the available system bandwidth by reusing the same spectrum between spatially separated cells with techniques such as co-channel cell deployment demands much on delay, delay variation, and synchronization particularly between macro- and small cells. Such stringent requirements makes the choice of backhaul solution difficult for a particular aspect, and hence the optimum solution comes from the holistic netwo rk view rather than a specific one. Figure 4.9 [ERA2014] shows the capacity and delay characteristics of various backhaul transmission technologies. From business perspectives, while choosing a backhaul solution the following should take into account: © RONY KUMER SAHA AND CHAODIT ASWAKUL

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    

The backhaul solution must not be costly to plan for, install, operate or maintain. The backhaul solution must match the anticipated volume of data traffic that the network may in near future to handle The backhaul solution should support the required network performances. The backhaul solution can serve any site location if possible, which is particularly helpful for small cells deployment and will maximize the return on investment The backhaul solution should not limit the QoE of users regardless of cells UEs are connected with for uniform experience to maintain the fame of operator’s brand.

The backhaul solutions varies with requirements such as scalability, cost-effective, and QoE. The scalability of a network can be achieved through the well-defined and carefully selected a set of backhaul technologies such as line-of-sight (LOS) and non/near LOS (NLOS) microwave link on higher frequency bands [ERF2013] such as (E band), NLOS microwave on the sub-6GHz frequency bands, point-to-point (P2P) optical fiber, point-to-multipoint (PMP) optical fiber, catego ry 5/6 LAN, and digital subscriber line (DSL). Capacity

WDM PTP fiber

LOS/NLOS microwave Category 5/6 electrical Ethernet GPON

VDSL2 NLOS PMP sub-6GHz ADSL2+ Ethernet over power

Delay

Figure 4.9 Capacity and delay characteristics of various backhaul transmission technologies. Backhaul solution addressing network operator’s cost is to choose solution that provide cost-effective and future-proof performance. One way is to limit the number of possible backhaul solutions and also new sites such that network management becomes simple to keep down the total cost of ownership. Further, since the rental cost itself constitutes up to half of the overall site costs [ERS2014], by deploying tightly coordinated backhaul solutions, total cost per site can be reduced. Moreover, for small cells where the capacity requirement is less, the backhaul solutions can be provided with lower performance technologies such a DSL or unlicensed microwave bands, particularly for initial backhaul solution with an upgrade provision for future. Also, for small cells multi-vendor environments are not recommended because of higher operations, administration, and maintenance (OAM) and interoperability costs. For outdoor deployments, wireless and fiber backhauls are good choices. Smalls cells can be connected to the macro network via LOS microwave or fiber. For a cluster of small cells network, wireless transmission media can be chosen, however, the clustered small cell network should be connected to the macro network via fiber or LOS microwave at higher frequencies (e.g., E-band) backhaul to provide high speeds. If LOS solution is not feasible, NLOS at higher frequencies can be used for small cells [ERF2013]. For short distant communication, unlicensed 60GHz can be used for high speed and traditional NLOS can be used for application in the final link of the network for capacity and delay constraints. For indoor deployments, reuse of existing copper and fiber can be used whenever possible. Very high -speed digital subscriber line generation 2 (VDSL2) can be used for backhaul over copper lines. Moreover, recently indoor frequen cy solutions can be used as backhaul through the backhaul with the traditional backhaul carries this traffic back to the access points. If backhaul solutions is to address the QoE, one way to do so is ensure proper coordination between radio nodes and layers which requires high performance end-to-end backhaul solutions such as fiber or LOS microwave link, particularly for links from the macrocells whenever possible. If fixed backhaul solution is not available, wireless link can be used as © RONY KUMER SAHA AND CHAODIT ASWAKUL

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default choice and NLOS can also be considered for greater degrees of freedom. To support multi radio access technologies (RATs) such as LTE and WiFi the shared backhaul solution needs to have QoS functionality.

4.6.2 Practical Deployment Scenarios and Choice of Backhaul Solutions When addressing backhaul challenges in HetNets, flexibility is key. There are usually several options for choosing any backhaul solution for each small cell deployment situation each with some merits and demerits. In the following few practical deployment scenarios are outlined and possible backhaul solution alternatives are given based on characteriz in g each scenario in terms of technical requirements. On City Street, there are mainly two deployment scenarios such as an indoor environment (e.g., indoor hotspot) and an outdoor environment (e.g., bus stop). For indoor hotspot, usually a picocell is deployed which can be connected to the macrocell over existing DSL line backhaul, however, based on capacity demand, and environment profile fiber or LOS microwave can be considered. For almost all other indoor small cells, almost any backhaul medium can be used because of short coverage and less coordination requirements. For in-building, telephony cable, copper category 5/6 cable are seemed to be most likely used backhaul media. The transport media to the building can be a mix of DSL copper, passive optical network (PON) fiber, and LOS microwave at E-band. However, if available, fiber should be preferred to other alternatives for high capacity and low latency. For outdoor environment, if the operator deploys a microcell, it should be connected to the macrocell via LOS or high performing NLOS. Fiber is preferable if available. However, NLOS at s ub6GHz can be used if LOS or NLOS at higher frequencies is not possible. Large, open, indoor hotspot such as railway stations or shopping malls are usually characterized by very high capacity demands, high rates of mobility, and much sources of interference. To address these performance requirements, a central base band solution with RRHs and localized distributed passive antennas along with WiFi areas could be deployed. In such scenario, the RRHs should be connected via fiber backhaul with the baseban d processing system. The baseband unit and WiFi can be aggregated to an indoor pico-gateway. The backhaul to the building usually should be fiber or LOS microwave at higher frequencies such as E-band. For offices and high rise buildings, coverage are usually less in the lower floors as compared to the top floors from the macrocell. Radio isolation between Floors is often good at about 20 dB. Hence DAS or multiple small cells can be used inside the building more in the lower floors to ensure indoor coverage. Typical backhaul for the building are fiber or LOS microwave. A large outdoor hotspots coverage such as a town square can be characterized by high capacity demand and significant sources of interference. In such an area, typically a small macrocell to cover the square, using an antenna-integrated radio solution complemented with a microcell and a micro RRU solution for the street coverage are used [ERA2014]. Because of high coordination requirements, fiber or LOS microwave backhaul for the macrocell. For distributed radio units, dedicated fiber backhaul for each unit should be used to connect to the remote baseband unit.

4.6.3 Backhaul Networks for CoMP In Figure 4.10, a schematic of a tree-type connected node HetNet with overlaying small cells is shown [VJU2013]. Each macroBS is 3-sectored and connected to another BS via X2 backhaul small cells are connected to a host macroBS. The branch in the tree is split and the host serves as the aggregation point (AGP). In LTE, there are two interfaces, including S1 and X2. S1 conveys user data from the aGW, entrance to the evolved packet core (EPC) networks, [VJU2013] to each BS and X2 is responsible for exchanging mutual information between cells. In a tree like Ethernet based physical realization of LTE backhaul architecture is shown in Figure 4.11 [VJU2013] where in each link, both S1 and X2 traffic are multiplexed. Clustering or a cooperating set can be formed static and dynamic manners. Cells in a cooperating set in JT-CoMP exchanges data to each other which can be handled using packet switching defined in the VLAN protocol IEEE 802.1Q [WKR2010]. Each cluster is tagged with a VLAN ID and is preconfigured at the network using the forwarding table of AGP [VJU2013]. Note that UEs in the JT-CoMP need to send CSI to all the cells in a cluster.


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smallcell

AGP

AGP

AGP

Inter-site Backhaul

AGP

Macro site

Macro cell

AGP Figure 4.10 Backhaul in HetNets for cooperation.

Core Networks

aGW S1 1, 2, 3

X2 ext

AGP 2-3 -1,

BS1 S1 3

X2 3-1, 3-2

X2 2

BS2

1-2 , 13

1

X2

S1

S1

2

BS3 Figure 4.11 Physical implementation of LTE backhaul networks. In dynamic flexible clustering, preferably applicable for addressing the mobility of an UE, an UE in a serving cell senses all the cells with Signal quality in a predefined threshold window less than the serving cell and sends the top -N such cells list to the serving cell. The N such cells form a cluster and tagged with a VLAN ID once the network decides on creatin g the cluster. The serving cell then sends the VLAN ID to the UE for letting it to know the cells in the cooperating set, which is required for the UE to sends the CSI tagging with the same VLAN ID back to the serving cell. The CSIs are then automatically multicast to other cells in the cluster over the network since the network has the knowledge of how cells are connected to each via backhaul [VJU2013]. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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With no cooperation between nodes, the upstream backhaul traffic is usually very small, however with CoMP, it is considerable because of local data exchanges among cooperating cells. If B is the bandwidth and rs is the downlink spectral efficiency in the serving cell, then upstream traffic from the serving cell can be computed as follows [VJU2013].

0.04 . B . rs , TUS   1.14 . B . rs ,

for no  cooperation for JT  CoMP

(4.4)

In the downstream, in addition to having received local data over S1 interface from the aGW, the serving cell receives data from all other cells in the clusters in CoMP mode which can be expressed as follows [VJU2013].

rcop 

N

r

i 1, i  n

i

(4.5)

Hence, the down-stream traffic, assuming the same overhead as in the up-stream for the down-stream can be given by [VJU2013],

1.14. B . rs ,  TDS   1.14 . B . rs  k . rcop ,

for no  cooperation for JT  CoMP

(4.6)

Where ri is the spectral efficiency of the ith cell in the cluster, N is the instantaneous cluster size and k is the inter-site ratio representing the fraction of cells reached via inter-site links and for macrocells, k can be considered as 2/3 [VJU2012] or 4/7 [VJU2013]. Based on the result presented in [VJU2013], it can be conclude that backhaul traffic grows linearly served by a macro-site with the increase of small cells and multiplicative when using cooperation. With all features in LTEAdvanced the backhaul traffic can be as high as 100 Gbps per macro-site.

4.6.4 Advanced Technologies and Impact on Backhaul Networks Addressing 1000x traffic in 5G than now, eventually needs introduction of new advanced technologies to address the high data demand. Dense Small cells, massive MIMO, mmWave spectrum, and cooperative communications are considered the major technologies for shaping the high data demand of 5G. Each of this technologies imposes burden on backhau l networks since the high data generated from theses technology use must be transferred to the core network. Deploying small cells densely will generate large signaling overhead from frequent handover, high interference, load balancing, etc. coordination between small cells can help reduce the interference, call-drop from handover, and near uniform load balancing between cells. However, a tighter coordination between cells will generate large overhead from information exchange to one another. Further, when coordination is adopted for joint CoMP, the same data is available at each node in the cooperating set by first transferring data at node followed by joint transmission. This results in user data along with others overhead to be exchanged via X2 backhaul interface. Moreover, as the data demand increases, the demand for control signaling on S1 backhaul interface increases consequently. Further, in cooperative MIMO, multiple based stations cooperate to exchange one another resource allocation and scheduling information decisions, resulting more signaling on the X2 backhaul. In massive MIMO, centralized in structure in contrast to cooperative MIMO, may install 100 or even greater number of antennas on the same site, requiring feedback reports for pre-coding matrices, CSIs, RIs from UEs. As the number of antenna in massive increases, so is consequently the feedback reports. mmWave communication is suitable for sho rt distance communication because of high path loss. Hence, it is considered mainly in indoor coverage as well as backhau l in future generation. More links on mmWave frequency are to be deployed to cover the same area, and hence more signaling to communicate and manage the backhaul on these high frequencies.

4.6.5 Backhaul Solutions for 5G Networks The backhaul solution for small cells in Dense HetNets can be implemented either centralized or distributed manner as given in [XGE2014] for 5G networks. In a centralized backhaul solution, smalls are deployed in the coverage of a microcell and are connected to the macrocell via mmWave wireless links. Each small cell transmits data to the macrocell and the microcell acts as the anchor or the central point for aggregating all data from small cells. The macrocell is connected to © RONY KUMER SAHA AND CHAODIT ASWAKUL

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the core network via fiber to the cell (FTTC) backhaul as shown in Figure 4.12 (top). However, in distributed backhau l solution, a group of small cells form a cluster or a cooperating set and one the small cells act as an anchor that collects all data from other small cells in the cluster in the cluster. The anchor small cell is connected the core via FTTC backhau l (Figure 4.12 (bottom)) [XGE2014].

Centralized backhaul solution

mm Wa

ve

w ir ele ss b

ack

ha

ul

lin

ks

Fiber-to-the-cell backhaul link

mmWave wireless backhaul links

Anchor small cell small cell coverage

Distributed backhaul solution Figure 4.12 Backhaul solution scenarios: centralized (top) and distributed (bottom). The backhaul traffic models for each solutions can be developed as before (equations 3-5). The backhaul traffic consists of a number of traffics, mostly the user generated data traffic in addition to the overhead backhaul traffic from S1 signaling, handover traffic on X2 backhaul between cells, and other management and synchronization traffic from wireless traffic. Considering ideal wireless backhaul links between small cells and between macro-and small cells, ignoring overhead from synchronization and management for wireless traffic, and assuming overhead 10% overhead from S1 signaling and 4% overhead from handover traffic, we can drive the total throughput requirement in bot h cases. We assume the average spectral efficiency of a small cell is

cen  smcell

and a macrocell is

cen  mccell

cen

cen

in centralized solution, and also Bsmcell and Bmccell

are the bandwidth for a small cell and a macrocell respectively. Then the upstream [JRO2012] and downstream throughputs of a small cell can be expressed as follows



cen smcell

cen cen cen  up  0.04. Bsmcell . smcell   smcell ,  cen cen cen  down (1  0.1  0.04) . Bsmcell . smcell   smcell ,

for upstream for downstream

(4.7)

Similarly the upstream and downstream throughputs of a macrocell can be expressed



cen  mccell 

cen cen cen  up 0.04. Bmccell . mccell   mccell ,

(1  0.1  0.04) . B


cen mccell

.

cen mccell



cen  down mccell

for upstream ,

for downstream

(4.8)

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Since there are N small cells in the coverage of a macrocell, the total upstream and downstream throughputs of the central backhaul solution at a macrocell can be expressed as follows. cen cen  up cen  up  N . smcell   mccell   total ,

cen  total 

 N .

cen smcell



cen  down mccell



cen  down total

for upstream ,

for downstream

(4.9)

In distributed backhaul solution, each small cell also cooperatively forward data to the anchor small cell. Consider the cluster size is N, and the average spectral efficiency of a small cell is

dis  smcell

dis

in distributed solution, and Bsmcell is the

bandwidth for a small cell in the cluster. Without including the anchor small cell, the spectrum efficiency of the cluster can be expressed as dis dis  cluster   N  1.  smcell

(4.10)

So the upstream [VJU2013] and downstream throughputs of a small cell in a cooperative cluster in distributed backhau l solution can be expressed a follows.



dis smcell

dis dis dis  up  1.14. Bsmcell . smcell   total ,  dis dis dis dis  down , (1  0.1  0.04) . Bsmcell . ( smcell   cluster )   total

for upstream for downstream

(4.11)

Hence the total backhaul throughput of a centralized backhaul solution and a distributed backhaul solution can be expressed as follows [XGE2014].



total backhaul

cen  up cen  down   total   total ,  dis  up dis  down ,  N  total   total





for centralized backhaulsolution for distributed backhaulsolution

(4.12)

In 5G, mmWave spectrum is considered as one of major enabling technology for high capacity. The low latency high capacity fiber backhaul is expected to be complemented with NLOS microwave links and LOS mmWave wireless links with peak capacity of 10-25 Gbps [CDE2014]. According to [CDE2014], the 60GHz band (between 57GHz and 66GHz) and E band (71-76 and 81-86 GHz) are well suited for mmWave wireless because of large bandwidth availability: 9 GHz and 10 GHz respectively and are license free or light-licensed and are available almost worldwide. For short links, the 60 GHz band can be a good choice as compared to E band because of free of license fee to make the network more costeffective. This high capacity mmWave wireless backhaul provides operators with an alternative solution to the traditional backhauling, by using multi-hop short distant links, with each hop of 100-200 meters. With multi-hop mmWave wireless links, the high capacity backhaul of up to 1 km can be viable [VBE2013].

4.7 Synchronization in HetNets The synchronization of time in HetNets is very important because of unplanned deployment of small cells. Time synchronization mainly affects the cooperation mechanism between cells for coordinated transmission among multiple BSs and to align receive signals. In addition, handover between cells is time sensitive and an accuracy of 1.5 micro seconds for time synchronization is required in time division based LTE. There a mainly three links that may be utilized for synchronization: backbone, satellite and cellular links [JXU2014]. Since small cells typically connected to the EPC via IP link, time synchronization (TS) could be possible to achieve using the IEEE 1588 protocol, i.e., precision time protocol (PTP) that employs time stamped packets between a server and its clients. However, in the context of small cells, PTP may face a lot of difficulties in terms of synchronization errors that results from different delays in both directions, delay jitter from observed delay variation in the Internet with traffic unpredictably, and new investments from deploying PTP-enabled router throughout the path between clients and the server. Hence, application of PTP in small cells does not seem feasible. Use of satellite source such as global positioning system (GPS) which is already matured can provide highly accurate time information over satellite links both frequency and time. GPS is commonly equipped with TDD macrocells. However, it require a good GPS –enabled receiver. Since small cells such as femtocells are expected to be deployed mainly to cover the indoor, the signal strength of satellite is not good enough in indoor coverage, resulting long time to synchronize or possibly not at all. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Use of cellular network for time synchronization can be performed by network listening to the synchronization signals from the neighboring cells which possibly synchronized already and perform timing adjustment. The network listening synchronization for small cells is specified in 3GPP. The small cells can listen to the primary synchronization signal (PSS) or secondary synchronization signal (SSS) or common reference signal from neighboring BS to perform time synchronization. This approach is cost effective since it does not impose any extra infrastructure requirements. However if the link between the macrocell and a small cell does not exist, he network listening cannot be possible. In this case, the small cell networks can in distributive manner perform time synchronization. Every small cell at each iteration sense the synchronization signal s and set a new time based its own time and the time from its neighboring small cells. Eventually, within a short time all small cells converge to the same time value.

4.8 Multi-Antenna Systems and Cooperation Employing multiple antennas at both the transmitter and the receiver is one of the most useful technologies that has been an active research topic in wireless industry to increase the spectral efficiency and the system capacity. There are several configurations of multi-antenna systems based on the objective to be addressed. Literally, when more than one antenna both at the transmitter and the receiver is used in a system, the configuration is termed multiple-input multiple-out (MIMO). To avoid ambiguity, we refer a BS as the transmitter and an UE as the receiver in the downlink and in the uplink an UE as the transmitter and the BS is the receiver in a cellular system. At system level, major concepts for multiple-antenna systems implementations are centralized and distributed. In centralized concept, a number of antennas are collocated at the same BS with a few wavelengths apart from one another. The centralized systems work best in terms of capacity and diversity improvement when the channels observed by each antenna is highly scattered such that the correlation between channels from the antenna array systems. However, in reality because of implementation constraints such as physical limitations at the transceivers a large number of antennas are to be implemented within limited space. This causes high correlation between channels, low degrees of freedom and hence results in a poor performances. However, if the same number of antennas are distributed geographically and are jointly processed at a central station via ideal backhaul links such as fiber and LOS microwave, the high degree of independent channels can be achieved [DCA2010]. Because of spatial diversity, the channel characteristics from different antennas varies significantly from one another for the same UE and hence results in high signal diversity gain and capacity improvement. Further the transmit power reduction and the path loss reduction are achieved from sp atial BSs diversity. This type of multi-antenna system is called distribute antenna system (DAS) [DCA2010]. In MIMO system, when there is only one user in the BS coverage, we refer it single user-MIMO (SU-MIMO) system. SUMIMO suffers from high channel correlation because of multiple antennas spaced apart in short distant both at the BS and UE. Further in SU-MIMO, the capacity is limited by the number of antennas at the UE since the capacity gain in MIMO is proportional to the lesser the number of antennas of the transmitted and the receiver and UE usually have less antennas that BS. To overcome this problem, high diversity in spatial channels can be achieved by employing MIMO to multi-user in a BS taking user randomness in distribution in a cell, and the resulting system is called multi-user MIMO (MU-MIMO). Even though the capacity can be improved in a BS with the number of users because of high degrees of freedom, interference from nearby BSs and UEs are the major bottleneck to capacity improvement. Cooperation between BSs can be exploited to address the interference to keep it at minimal level or in some cases, zero. However, cooperation needs tight synchronization between BSs and exchange of information via backhauls that results in large overhead in the backhaul network with the increase of cooperating set or the cluster size for cooperation. Note that our discussion for far has considered co-located MIMO (CMIMO) where all the antennas at a BS is located at the same place, the BS. We are interested mainly in BS antenna since the size if an UE is small and the number of antennas, eventually the exploitation of the degrees of freedom is less. In multiuser based C-MIMO (MU-CMIMO), a BS can send different data streams simultaneously to multiple UEs that are only within it coverage [XZH2013]. With no cooperation from nearby BSs no spatial multiplexing gain can be exploited in MU-CMIMO and hence no scaling of capacity with the density of BSs in the network. Spatial multiplexing can be thought of the num ber of parallel streams that can be formed between a transmitter and a receiver and is upper bounded by min {NT, NR} where NT and NR are the antennas at the transmitter and the receiver respectively as mentioned before. Usually a high data rate stream for the user is segmented to a number of low data rate streams each of which transmitted via different antennas in MIMO set. The number of parallel streams is limited by the upper bound as mentioned above. However, in MU-MIMO, an extension of spatial multiplexed is used where different data streams are usually assigned to different antennas for different users served by the same BS.


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With employing BS coordination, higher degrees of freedom can be achieved from having multiplexing gain from other BSs as well. This configuration is called Networked MIMO where the group of BSs in the cooperating set form a virtual massive multi antenna transmitter. In Networked MIMO, [XZH2013] different data streams from multiple BSs are simultaneously transmitted to multiple UEs within or beyond their cell coverage by cancelling cross-talk interference and hence achieves multiplexing gain that scales the capacity with the cluster size or the density of BSs in cooperation. This requires tight synchronization in terms of transmission time, carrier frequency and sampling clock-rate and user data (mustbe) sharing between BSs in the cooperating set for cancelling cross-talk interference. Since the overhead increases significantly with the size of the cooperating set, Networked MIMO is feasible for small networks. Note that it is typical that BSs are completely synchronized at the carrier signal level by using a reference signal, or they can use synchronization signal (PSS/SSS). Since BSs are spatially distributed and connected for cooperation via backhaul, Networked MIMO is also referred to as distributed MU-MIMO. Figure 4.13 shows different antenna configurations [RWH2013]. BS cooperation can also be exploited for signal diversity and interference mitigation. In this regard, 3GPP has considered CoMP mainly to address the interference and cell edge user performance improvement. With JT-CoMP, multiple BSs transmit simultaneously the same user data to an UE in addition to the UE’s serving BS at the same frequency-time resource such that the interference signals, that they are supposed to be otherwise, from all cooperating BSs now becomes the desired signal for combining at the UE receiver. Note that information the UE has first transferred to all the BSs in the cooperating set before joint transmission takes place. In CS/CB CoMP, only the serving BS sends the user data, however with a coordination of resource scheduling among other BSs such that they can either form proper beams or stop transmitting the UE. Hence, the performance gain for JT CoMP comes from the signal combining and the ICI nulling, whereas for CS/CB from avoiding the interferen ce through, e.g., beamforming. This results in better cell-edge user throughputs. Hence, as proposed in [ZZO2014], CoMP with MU-MIMO can also be exploited to the capacity improvement by taking advantages from spatial multiplexing gain of MU-MIMO and interference avoidance/nullification of CoMP. However, 5G is expected to provide 1000x data volume than the current network. In order to address the high capacity demand of 5G, more antennas, e.g., hundreds or multiple order of hundred, are expected to be deployed in 5G. This antenna configuration is called massive MIMO or large antenna systems. Massive MIMO relies on spatial multiplexing and hence it is assumed that the BS has the channel knowledge on both uplink and downlink. On the uplink, UEs can send pilots and the BS can measure the channel based on them for each UEs channel responses. In the downlink, channel measurements are not easy. Note that in FDD based current LTE systems, BSs send pilots to UEs, and UEs estimates the channels based on pilots and after quantized the estimated channels, sends back to the BS [EGL2014]. However, this not so easy for the case of massive MIMO, particularly for high-mobility users for mainly two reasons [ref]: it requires 100 time more resources bot for uplink as well as downlink to make sure of mutually orthogonal pilot between antennas for the downlink, and the proportional increase in the number of channel responses with the increase in the number of BS antennas for the uplink. That’s why massive MIMO is expected to be operated on time division duplex (TDD) mode where it can use the channel reciprocity between the uplink and the downlink [EGL2014].

4.9 D2D Communications and Cooperation In D2D communications, devices communicates one another without any intermediate nodes between them with the assistance from the network. The main reason for deploying D2D in 5G is that it can offload sufficient amount of data from the macrocell [SMU2014], particularly in the cell-edge area if macrocell, where signal quality between an UE and the macroBS is poor however, the signal between an UE to the other is better because of short distant between them. D2D works on the same cellular bandwidth and in governed by the network as in case of other small cells. D2D can achieve gains from short distant between UEs that can help achieve high data rate, low delay, and low energy consumption (low transmit power for short distant) [MWE2007] [HSC2011]; the reuse of the same spectrum to other UEs in the macrocell at sufficient separation help achieve high spectral efficiency [HSC2011]; and there is no need to use both uplink and downlink resources as in the usual case of cellular communications. Further, extension in coverage, deployment on the existing cellular infrastructure are attractive features of D2D from economic perspectives. However, as in the default scenarios in cellular communications, D2D communication links also suffers from both intra- and inter cells interferences. The cause of intra-cell interference is occurred between macro UE and the D2D UE (consider that only D2D is present in the coverage of macrocells, no other small cells there-in) mainly from the D2D receiver to the macro UEs when both types of UEs are assigned with the same resources.


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The inter-cell interference is occurred between D2D UEs and the nearby macroBSs surrounded round the serving BS (to which the D2D UEs are connected with). With coordination among macro -BSs the inter-cell interference at the D2D UEs, particularly at the cell-edge area can be overcome by proper resource scheduling and allocation decision exchange on the backhaul links between macroBSs. If required, D2D UEs can also sends CSIs to the interfering macroBSs in the cooperating set in addition to the serving macroBS, such that either JP or CS/CB CoMP can be exploited as required.

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The intra-cell interference for D2D UEs can be overcome by the serving BS using well-known algorithms such as zeroforcing (ZF) and minimum mean-square-error (MMSE) based on the CSI received from the affected UEs by employing appropriate precoder matrices for those UES at the macroBS since it has all the information of all UEs including D2D UEs and macro UEs in it coverage. With coordination the information of other macrocell’s UEs can also be known at the serving macroBS based the degree to which information are decided to be exchanged mutually. Figure 4.14 [SMU2014] shows CoMP for D2D communication. Inter-cell interference source

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Figure 4.14 CoMP for D2D communications.

4.10 Cooperation Strategy and Network Protocol Stack With an introduction of cooperation between network entities such BSs, a number of challenges arise from individual layer perspectives that need modifications on the existing non-cooperative networks. Cooperation can address the QoS improvement in terms of, for example, reliability, throughput, and service provision. Most of these improvements from cooperation comes at impacts on each layer. We will mention few of these impacts layer-wise that may need immediate attention when cooperation is employed in the network.

4.10.1 Physical Layer In physical layer, cooperation mainly deals with the actual physical wireless channel characteristics such as stationary and variable channels with time and (or) frequency and performances to achieve in that channel such as reliability, throughput, and seamless service continuity. When cooperation is employed for the purpose of reliability through diversity this usually can be address by cooperative relaying. Mainly, there are three relaying strategies in literature for cooperation of a relay with the DeNB and UEs, including amplify-and-forward, decode-and-forward, and coded compression. Each of the strategies have alread y discussed in a previous sub-section. A major challenge that these strategies face is that the channel coefficients between the source node and the cooperating nodes should be known for optimal decoding at the destination node [HAS2005]. The challenge become more difficult when an UE is on the move because of rapidly changing channel environments. Hence, techniques for adaptive estimation of the channel states with th e UE-mobility context specific time constraints should be considered in the implementation of these strategies. In addition cooperation extends the hardware complexity at the destination nodes since multiple copies of the same data may have to be decoded, transmitted from multiple nodes (e.g., for diversity, both DeNB and relay transmit the same data). Hence, to address it, new modules such as sample buffer, combiner have to be included in the physical layer. The sample buffer can store the multiple copies of the same data and then combiner can merge them with new received data [XCS2010]. However, if the data received at the destination is © RONY KUMER SAHA AND CHAODIT ASWAKUL

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newly coded as in the case of coded compression, linear combination of multiple copies cannot be stored in the sample buffer and the buffer size increases linearly with the number of received copies for the same packet [XCS2010]. If the cooperation is aimed at increasing system throughput with resource aggregation and seamless service provision, the nodes should be able to transmit or receive multiple data concurrently. If the data belong to multiple network technologies (e.g., LTE, WCDMA, WiFi, etc.), an UE needs multiple radio interface to decode these different data from different access technologies. One approach can be to implement multiple physical layer interfaces to support multiple transmission technologies in parallel. Other approach can be to use one physical radio interface with discontinuous orthogonal frequency division multiplexing (DOFDM) as shown in Figure 4.15 [YSS2012] as physical transmission technology such that a node can transmit or receive multiple data in parallel using multiple channels for multiple radio access networks. One way to view it to transmit or receive data each for one specific channel where a CC or more than one CCs (e.g., 60MHz for LTE-Advanced, channel 1 in Figure 20 can be used) is the system bandwidth for a specific radio access technology and can use one channel at the UE. DOFDM is a kind of non-contiguous carrier aggregation OFDM technology, where a single radio can access different discontinuous spectrum segments to aggregate into one wider bandwidth, the similar technique that has been adopted in 4G LTE-Advanced for wider bandwidth in non-contiguous CA. note that the span of CA in DOFDM is limited [LIJ2010], as in the case of LTE-Advanced the maximum number of 20 MHz spectrum can ne aggregated is 5. DOFDM is used recently and widely in cognitive radio networks [CDZ2008] [PJD2005] because of its flexibility in dynamic resource allocations and low adjacent subcarrier channel interference [ZYL2008].

Channel 1

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4.10.2 MAC Layer The MAC layer plays an important role in answering many questions in cooperative mobile communications, such as when to use cooperation, whom to cooperate with, and how to select the cooperation entities along with how to reduce the inte rference [WZM2012]. Cooperation mainly is considered for throughput improvement, energy efficiency improvement, interference mitigation, and service continuation. Since cooperation gives benefits at the expense of network overhead and added complexity, an adaptive MAC protocol is inevitably developed to make decision whether or not to consider cooperation which largely depends one cooperation scenario. If cooperation is for reliability, the achievable throughput can be considered at the source node and the decision may depend on the comparative result from the achieved transmission rate with cooperatio n and direct communication [HSW2009]. Also when the direct communication is not able to provide necessary throughput, or is unable to provide service continuity, cooperation should be considered. Since cooperation decision depends on physical layer outputs such as CSI, RI, PMI, etc., a cross layer approach between physical and mac layers are required [HSW2009] [HSH2011] [HSH2008]. Cooperating entity selection depends on mainly factors such as the number of entities, entity selection mechanism and the simulation algorithm to select the optimal entity. For single entity based cooperation, selection can be based on the maximum transmission rate among entities. Even though, this selection method is simple but lacks from QoS requirement. For multiple entity based cooperation, a number of issues should be considered while selecting entities for cooperatio n such as interference and cooperation overhead. With the number of entities, interference increases proportionally, affectin g mainly the reuse of the same frequency spatially. Moreover, as the number of entities increases, the overhead signaling also increases, which can be overcome by limiting the of entities in the cooperating set. This has already been proposed for CoMP where a group of BSs coordinate each other via backhaul to minimize the overhead and to mitigate the interferen ce © RONY KUMER SAHA AND CHAODIT ASWAKUL

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mainly in the cell-edge areas where most users are interfered by the adjacent BSs. Further, a trade-off between the number of entities cooperating for the parallel links and the channel correlations between links should be considered into accoun t to make most link diversity gain to improve signal strength at the receiver. When Selection of entities is done in a centralized manner, the central controller usually has the global view of the network and selection can be done optimally. However, since each entity has to feedbacks the channel conditions to the controller, a large overhead is expected from this approach. Also there is a possibility for a single controller failu re and the outage of the whole network. In this case, logically centralized but physically distributed multiple controllers can be adopted such that each controllers can communicate each other and hence information has become distributed. This approach is adopted in software defined network (SDN) and an extensive effort is ongoing to use SDN on the cellular network, primarily in the core network, mainly lead by the OpenFlow protocol. In distributed entity selection approach, the first issue to fins the capability of entities for cooperation. A utility function including performance matrices such as throughput, power consumption, etc. can be applied to measure the capability of entities. To select the cooperating entities based on the result out of that, a timer based solution [HSW2009] can be employed where each entity has a timer set inversely with the capability of the entity. Hence, the entity with the most capability will be exhausted its timer the earliest among all entities. This way based on the cooperating set, let’s say with size N, the first N exhausted entities can be selected. The 3GPP LTE-Advanced system has already adopted the distributed entity selection, where each entity is a BS, and a group of BSs (cooperating set) are connected to each other via X2 backhaul , forming a cooperative distributed antenna system (DAS) for CoMP operation. In DCS CoMP where the serving cell is dynamically selected or in hybrid JP and CS/CB where a subset of BSs of the cooperating set are selected to transmit jointly to mitigate the interference at the UEs.

4.10.3 Network Layer The Network Layer is responsible for defining an optimal routing protocol to send data from the source nodes to the destination nodes through the cooperating nodes. If concerning with the reliability, a number of issues are concerned with the network layer while defining routes in cooperative communications. Different from traditional point -to-point communication where a series of intermediate nodes participate to transfer data from a source node to a destination node, the cooperative link consists of a set of transmitting nodes in the cooperating set following cooperation principle to deliver data to a set of receiving nodes [WZM2012]. Hence a route from the source to the destination may consists of more than one link. There are generally two types of cooperative links such as multi-input single-output (MISO) and multiple-input multiple-output (MIMO). In MISO, multiple source nodes communicates cooperative to a single destination node, whereas in MIMO, multiple source nodes and multiple destination nodes communicates. The routing definition for such multiple-terminal links need great attention to improve the reliability of communication. MISO and MIMO techniques have already been implemented in 3GPP LTE, where multiple antennas at the BS and multiple antennas at the UE have been adopted. Note however that, with the definition of a node (a physical entity, e.g., UE, BS, etc.) as assumed in this article, multiple antennas at the BSs (MSs)form a single spatially multiplexed link, irrespective of the number of antennas at the BS and at the UE. An example MISO and MIMO schemes is given in Figure 4.16. Three BSs coordinate each other in JP CoMP to UEs. We consider that each UE has a single antenna, however BSs may have more than one antenna. We also consider only one aggregated spatial link between a BS and an UE. When an UE is severed by multiple coordinated BSs is the case for MISO link, and when more than one UE are served by more than one BSs is the case of MIMO. Note that a cooperative MIMO consists of a number of cooperative MISO link as a special case. Another major issue for cooperative routing protocol design is find a good trade-off between optimality and complexity [WZM2012]. According [MDE2009], cooperative routing involves taking decisions at multiple-stage and decisions are taken at each stage as the data routes from the source node to the destination node. As proposed in [JZH2008] [MDE2009], with a link cost definition for each link, e.g., transmit power to send the data to the next node in the cooperating set such that the decision on optimum route will be the one that gives the minimum sum of the link costs. For example, if the transmit power to server a UE in the macroBS coverage needs more power than a picoBS near the UE, rather than choosing the direct link from the macroBS, the UE can be served by the picoBS since the transmit power of picoBS is usually very less than that of a macroBS. This is one of the reasons because of why low power BSs are deployed in the coverage of macroBS. However, this dynamic routing algorithm suffers from high complexity with the number of cooperating set and difficult to implement. One solution to this problem is to make the cooperating nodes number limited such that complexity can be reduced to a tractable level, and the implementation can become viable. In [AIB2008], in order address the complexity, a sub-optimal cooperative routing algorithm that uses a direct point-to-point link and a cooperative MISO link of any © RONY KUMER SAHA AND CHAODIT ASWAKUL

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number in cascade has been proposed. This approach can be used in backhaul networks where a combination of optical fiber and multiple mmWave link as MISO can be considered. An example scenario is where small cells communicates to the macroBS via mmWave backhauls and the m acroBS is connected to a point-to-point optical fiber to the aggregated gateway (aGW) point to the core network. Similarly other macroBSs are also connected to the aGW and the aGW is then connected to the core network via a point-to-point high speed optical fiber (Figure 4.17).

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Further, when multiple traffic flows are coordinated by the same node, interference could be severe with the increase in the number of traffic flow. An example is when the antenna separation at the BS is not sufficient, there is hi gh probability of leaking information from one antenna to another, consider that the antennas are carrying different symbols, e.g., for spatial multiplexing. The symbols could be of the same user or different users. Even with the same user but multiple streams are transmitted in parallel the streams at different antennas interfere each other because of emission of energy from one to another. This causes degradation in overall network throughput. Hence, proper attention must be taken when designing routing protocols such that intra- and inter users interference will be minimized. There are three categories of multi-path routing: node disjoint route, link disjoint route, and non-disjoint route based on the configuration of links and nodes in the making of a route. If no nodes or no links between different paths are common is called node disjoint; no links but nodes may be common between paths is called link disjoint; and if both node and link can be common between paths in called non-disjoint route as shown in Figure 4.18. Disjoint routes are advantages from most resource aggregation to improve throughput and from high level of fault tolerance, however are constrained from difficulty in finding an optimal route. Further, multi-path route maintenance, particularly new route discovery in case of failure of an existing one is another challenging task. Frequent route discovery can help improve QoS from service discontinuation but at the expense of more overhead. Hence, a trade-off between QoS and network overhead resulting from route discovery should be taken into consideration in the routing algorithm development. For example, as in CS/CB CoMP, it may be wise to investigate whether or not the CSI feedbacks from UEs in JP CoMP are to be provided to all cooperating BSs or if it is sufficient enough to provide to a subset of BSs as long as the throughput does not deteriorate considerably. In that case, as proposed in 3GPP a hybrid of CS/CB CoMP and JP CoMP can be considered to reduce the CSI overhead. Hence, in cooperating routing protocol design, all these cooperative strategies should be exploited for an optimal multi-path route establishment and discovery. The multi-path routing usually originates difference in delay of the received signals along multiple paths which is referred to as differential delay [XCH2009]. The delay difference causes some data to earlier than the others require the reordering of the data at the receiver. Data must be stored first for reordering in a buffer at the receiver. Hence the buffer size should be considering while designing a multi-path routing, particularly for highly scattered environment where the multi-path propagation effect is very significant. Typically the delay spread is the most in urban area of 3 µs and hence is more susceptible to multipath routing effect. In addition, the multi-path routing can cause significant interference when paths are assigned with the same frequency for transmission, i.e., co-channel interference effect that results in degradation of the overall network throughput. The most straightforward way to overcome the interference from co -channel is to assign each path with a different frequency, orthogonal frequency allocation to each path from another. Care must be taken when designing multipath routing on different frequency since it takes much bandwidth from the system. Network coordination can help to create parallel channels to improve throughput even with the same frequency reuse and hence some mechanisms can be exploited to improve the throughput at the expense of none or least bandwidth use in the design of multi-path routing algorithm. Source

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4.10.4 Transport Layer In transport layer, cooperation to improve the channel reliability through diversity are mostly performed at the physical, MAC and network layers and hence, the transport layer does not have any impact on reliability. However for throughput improvement through resource aggregation needs multi-path routing. Multipath routing needs separate IP addresses for the source and destination nodes. Traditional transport layer protocol such as transmission control protocol (TCP) and user datagram protocol (UDP) supports cannot support multiple IP addresses for a single entity. TCP allows only one IP address per endpoint [TCH2006]. This problem can be addressed by using multi-homing feature to the transport layer protocol. Multi-homing features allow a transport layer protocol to establish an end point with multiple IP addresses such that multipath routing can be possible between end points [TCH2006]. Stream control transmission protocol (SCTP) is one such transport layer protocol with multi-homing features [RST2000]. In 3GPP LTE, an UE is usually assigned with a separate IP address for separate packet data network (PDN) (IP network, IMS, etc.) and aggregate throughput is controlled as part of QoS by UAMR QoS parameter. Hence, without multi -homing capability in transport layer protocol an UE cannot get access to multiple PDNs. One drawback of SCTP is that it does not support simultaneous transmission of data packets to multiple IP addresses since it uses one path as a primary and other paths are used as either for backup or retransmission. However, to aggregate resource for throughput improvements needs simultaneous transmission through multi-paths. But simultaneous transmission arises a number of issues such as path assignment, i.e., how to assign each packet on a path based on matrices, e.g., bandwidth availability, round trip time (RTT), etc. Packet reordering is another issues that need attention which otherwise creates unnecessarily overhead from retransmission when SCTP or TCP is used as transport layer protocol. Out of packet received from the fact that different path have different delay and bandwidth and when simultaneously transmitted packets travel through these paths received at the receiving end out of order, stored in a buffer and reordering of packets is th en performed for in sequence delivery to the application layer. Cooperation for seamless service provision also imposes some challenges on the transport layer. If an ongoing path is broken a new path is considered when we assume seamless service provision. However, the characteristics of the new and the broken one do not necessarily have the same characteristics such as delay, bandwidth-delay product (BDP) and raises issues that are to be addressed. For example, from low delay path to high delay path hand off, the retransmission time out (RTO) is not able to cope with the high delay and hence results in retransmission of the same packet by the TCP sender causes network resource wastage of use. However, for reverse case, i.e., from the high delay path to the low delay path handoff, packets arrives earlier than the previously transmitted packets by high delay path results in reordering of the received packets at the destination node. In the case BDP differentiation, when handing off from a low BDP path to a high BDP path, inefficient utilization of the new path is expected since the TCP sender is not able to come up with the high sending rate. In the reverse case, i.e., a high BDP path to a low BDP path will cause buffer overflow of the new path and hence los s of packets because of the TCP sender sending data at high rate in the new path. A number of solutions to address these issues are existing in the literature such as [LDA2008]. Hence, the design of routing protocol should take these issues into consideration of the transport layer. Overall, many issues are still remained open in perspectives of network cooperation such as overhead from cooperation , support of user mobility, cross layer design for tighter integration across all layers considering issues as discussed, and business model for accountability and billing systems [WZM2012].

4.11 Summary In this chapter, we have detailed on the existing cooperation techniques in 3GPP LTE and their advancements to meet the upcoming 5G networks. Cooperative mechanisms for relay technologies, network nodes, carrier aggregation and scheduling, interference management, joint transmission and reception, and multi-antenna systems have been discussed along with highlighting relevant aspects of cooperation. We have also exploited the requirements from cooperation such as backhaul networks, feedback schemes, and network synchronizations. Cooperation in new enabling technologies such as D2D communications to address proper communication between devices and interference manifestation between network nodes from D2D communications have been discussed. We have also addressed the impact of cooperation on various layers such as physical, mac, network, and transport layers and have discussed the relevant modifications on the current networks layer-wise. The performance of cellular networks with or no cooperation employed on the networks are discussed elaborately based on existing literature, particularly in terms of spectral efficiency and network capacity which may serve as base to investigate cooperation principle in future networks for possible enhancement of network mechanisms and operations. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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CHAPTER 5 WIRELESS SOFTWARE DEFINED NETWORK © RONY KUMER SAHA AND CHAODIT ASWAKUL

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5.1 Introduction Current wireless networks are inflexible, non-scalable, and complex in architecture that makes it hard to change to adapt with emerging needs of data applications. Data traffic to Internet through wireless access networks is expected to grow faster than traditional fixed access networks based Internet traffic [STO2014]. According to [CIS2013], it is expected an increase in mobile data traffic by a factor of 13 between 2013 and 2017. In order to address this high demand of data traffic, network operators usually consider reducing the distance between the base stations (BSs) and user equipments (UEs) to as short as possible to improve the signal strength at the UEs and to serve more UEs. One ways to do this is to reduce the cell coverage through deploying sm all cells such as microcells, picocells, and femtocells - a usual practice that current operators are employing called heterogeneous networks (in short HetNet). However, deploying a large number of small cells to cover a particular area rather than choosing a large one, e.g., macrocell results in multiple issues such as increase in interference from more closely neighboring BSs, more frequent handovers, and complexity in radio resource and mobility management to mention a few. One of the noticeable characteristics of current mobile networks is that both control decisions and processing tasks exist in the same equipment, e.g., BS, Serving gateway node (SGN), PDN gateway node (PGN) in LTE. This implies that the current mobile networks are based on distributed control and data planes, where each entity distributed over the network is responsible for both decision making and processing task execution. This distributed nature of current mobile networks results in manifold challenges such as complexity in netwo rk control and management (e.g., mobility manager needs to coordinate more with the increase in users), network efficiency (e.g., difficulty in updating the existing equipments with advanced solutions), non -scalability (e.g., PGN gets overloaded with the increase in traffic through it), non-evolbilty (e.g., less scopes in creating service differentiation from one competitor to another), and inflexibility (e.g., to introduce new features, operators need wait for vendor specific solution). Hence, to address the high demand of mobile traffic by overcoming these challenges, network operators seek to new solutions [STO2014] but preferably with the existing networks since for example, LTE has just been deployed worldwide that costs huge investment. SDN concept that was originally developed for wired network solution can be applied as a new solution to wireless networks in order to address these aforementioned issues. For example, since current network elements hold both the control plane and data plane functionalities, by employing SDN approach these two planes can be separated, i.e. the control plane functionalities from the data plane functionalities. SDN then shifts the whole control functionalities network wide to a logically centralized entity called SDN controller such that the current distributed control functionality can be overcome. The controller is the only responsible entity for the overall network operation, control and management tasks. However, adopting SDN principle in wireless medium by capturing its characteristics is not straightforward and challenging because of the presence of inherent uncertainty in the wireless medium, as compared to its wired medium counterpart. Several issues must be resolved for WSDN implementation as a viable technolo gy. Firstly, research experimentation facility for wireless medium is limited and in some cases almost none. This is because of huge investment for testbed implementation, resource unavailability that are compatible with the wireless features for SDN compo nents in the commercial market, insignificant standardization attention such as 3GPP/LTE. Secondly, mobile networks such as LTE has just been deployed, and operators are still under observation of how LTE is accepted by the users and mostly unaware of SDN adoption on the current networks. Thirdly, the adoption of SDN in wireless networks has just been on the-fly concept, with insignificant research contributions in the literature both from academic and industry. Hence, relevan t concepts, tools, and solutions for solving a particular issue is not something in-hand.


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In this chapter, we first present the fundamental concept of SDN and its components. We then present the application of SDN in wireless networks through several existing architectural proposals in brief for LTE, WiFi, and WiMAX wireless networks. Finally, Major implementation specific issues, challenges, and requirements are outlined for viability of WSDN.

5.2 SDN Concept The concept of SDN is simple and based on split architecture where [STO2014]:    

Network control functions are separated from the network forwarding functions. The network intelligence is moved on to a logically centralized single entity called SDN controller. The controller maintains a global abstracted view of the network on which control and management applications work. The controller communicates with the forwarding nodes with a standard protocol, e.g. OpenFlow. The network operator can control and manage the network on requirements by inputting necessary commands through the SDN control and management programs that lie in the controller and hence free from vendor agnostic process.

Hence, by enabling the separation between control and management plane functionalities from the forwarding functionalities, SDN provides simplicity in network control and management and offers innovation in the network. Because of the programmable feature of the controller, these networks are referred to as Software defined [JQA2014]. Please note that, an extensive argument has been discussed and stressed that SDN is not about performance improvements; it is about new way of networking that allows programmable network capabilities. Even according to [8], the overall efficiency in using network resources might be decreased while considering higher labels of abstraction with SDN adoption.

5.2.1 SDN Architecture Figure 5.1 [ONL2014] shows the typical SDN architecture. SDN consists of three planes: forwarding plane that includes forwarding elements, control plane that includes network operating system and network hypervisor, and application plane that includes network control and management applications. In addition, there are two interfaces: south bound (e.g., OpenFlow) and north bound (e.g., XML). In the following, briefly we explain each components of SDN. Network operating system: a network operating system (NOS), like ordinary computer operating system (OS), provides the ability to observe and control the network [NGU2008]. NOS does not manage the network but merely provides a programmatic interface. NOS also keeps the network states and provides a global view to the controller. NOS has basic applications that are responsible for global network view creation and management. NOS and applications run on servers. Example NOSs are NOX, POX, etc. Applications: Applications are control and management programs that are usually implemented on top of NOS and perform all control and management tasks. The global network view contains the results of NOS, and applications use it for control and management decisions. Example applications are routing, mobility management, etc. Hypervisor: Network hypervisor is used to virtualize the physical resources into a number of virtual resources such that multiple users can use the same physical resources concurrently with intervening one another. Hence, it is a solution and not a basic part of SDN architecture. Hypervisor is a software that is installed in a server. An example hypervisor is FlowVisor [RSH2009], developed in approximately 7000 lines of C, sits logically between the controller and the forwarding switches and acts as a proxy such that all traffics to and from the controller and the forwarding switches pass through the FlowVisor in order to enforce proper policy on packets in each flow to provide isolation between virtual network resources.


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SDN Architectural Planes and Interfaces

Applications

Examples

Routing Mobility management

Northbound Interface

XML

Network Operating System

NOX POX

Network Hypervisor

Southbound Interface

Forwarding Switches

FlowVisor

OpenFlow

OpenFlow Switch

Figure 5.1 Typical SDN architecture. SNMPVisor: OpenFlow does not provide a way to configure datapaths, e.g., setting power levels, allocating channels [KYA2010]. This is done by the SNMPVisor - a command line interface that runs alongside FlowVisor to allow experimenters to configure their own virtual resources. It is also a solution for datapath configuration and not a basic entity in the SDN architecture. South bound interface (OpenFlow): South bound interface provides necessary medium to communicate the controller with the forwarding switches. A well accepted south bound interface is OpenFlow. OpenFlow is a standardized protocols that provides a way to communicate the controller with the data path using match-action rules [KYA2010]. When a packet arrives at the data path switch, its packet header is first matched with a flow entries in the flow table resided in each dat a path switches and the corresponding action is taken on the packet following the OpenFlow specification for match -actio n rules such as forward, drop, modify, or send to the controller the packet. OpenFlow is a flow based protocol and is commercially available. North bound interface: This interface allows applications to communicate with the NOS. There is no standardized north bound interface yet. XML can be used as north bound interface. Forwarding switches: SDN forwarding switches are responsible for switching the packet from the ingress port to the egress port. An example forwarding switch is OpenFlow switch that contains a forwarding table incorporating a number of flow entries. Each flow entry has three fields: a packet header defining a flow, the action defining how the packet sho uld be processed, and statistics that keeps record on the number of bytes and packets in each flow and the time since the last packet matched the flow [NMC2008]. Each flow is controlled by the controller. Figure 5.2 [NMC2008] shows an idealized OpenFlow switch.


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OpenFlow Switch OpenFlow Protocol

Secure Channel

SSL

Controller Software part Hardware part

Ingress Port

Packet Flow In

MAC source

MAC IP IP TCP destination source destination destination

Flow 1 Flow 2

Action field

Statistics field

Forward

Count x

Drop

Count y

Egress Port

Flow Table Packet Header (match field)

Packet Flow Out

Figure 5.2 Idealized OpenFlow switch.

5.3 WSDN Architecture In wireless networking, there is no single dominant technology; rather several technologies are deployed based purposes, addressing a particular scenario. Cellular mobile technology based networks serve usually high -speed mobile users to static users with nationwide networks connectivity, e.g., LTE. WiFi technology based WLAN networks usually provide wireless connectivity with low-speed pedestrian or stationary users in localized environments, e.g., campus networks, enterprise networks. WiMAX technology based networks usually serve a large area, e.g., metropolitan areas with high-speed to static users and is extendable to nationwide networks connectivity. Diversification in technology, distributed network management paradigm, unpredictable wireless medium, and multitude user requirements make the current wireless networks hard to manage. SDN, with its capability to separate the control plane from the data plane and controlling the data plane by providing physical data plane network abstraction to the control and management programs, can provide flexibility and simplicity in wireless network control and management tasks. More importantly, it is the physical network abstraction - the key in SDN that makes significant impact on the controller decisions, i.e., control algorithm's effectiveness and hence, the overall efficiency of the SDN based networks. Proposition 01: Control plane and data plane can be separated. There is always a control plane and user plane in most network entities. We can separate all the control plane functionalities network wide shifted onto one centralized component and leave the data plane as it is, just as dump device and can control these data plane device functionalities from the centralized component. Explanation: In current wireless distributed network paradigm, where network intelligence and processing functionalities in most network entities (e.g., SGN, PGN, and RAN in LTE) are distributed across the network. Through proper interface (e.g., OpenFlow), the intelligence part of these network entities can be separated from the processing functionalities, and be moved to a logically centralized entity – the controller. All the management and decision applications can be implemented on top of the controller and can communicate with the physical user plane of these entities through a proper interface (e.g., XML). Proposition 02: SDN controller is technology independent. Given the physical network abstraction of any wireless technology (equally applicable for wired as well) such as LTE, WiMAX, or WiFi, the principle of operation of SDN controller is the same. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Explanation: The controller simply takes decisions based the abstraction given to it. So, SDN controller is technology independent. Even though the underlying network abstraction of LTE, WiMAX and WiFi are different, it does not have any impact on the controller operation since the controller works simply on whatever the underlying abstraction it is provided with. Given simply the abstraction of the underlying physical networks (probably in terms of graphs comprising of nodes and the links associated with), the SDN controller takes necessary decisions, based on the operators goals that are input through applications. Proposition 03: Abstracted network can be segmented, and modular based implementation can be introduced. Based on the abstraction, a modular based implementation in SDN is made simple. Abstractions allow the controller to segment the network in parts - modules based on specific characteristics of nodes and links that a particular segmented network belongs to. Explanation: In mobile communication networks, based on the geographical region such as urban, suburban, rural, dense (hotspot) areas, we can separate the global view (abstraction) of the underlying network. And since each region has different characteristics replicated in the abstraction of that part of the network, the SDN controller can set modular based implementation such that there is a module for each region: urban-module, suburban module, rural-module, DenseNet module, etc. Each module is responsible for that particular region and updates itself according to the network abstractio n changes for that regions. This can simplify the controller decision and network management tasks since if there is any changes in need in a particular region, only the corresponding module needs to be updated, leaving the rest of the network update unchanged. A number of WSDN architecture have already been proposed, few consider the legacy LTE networks almost unmodified; while others propose change in the current networks. In this section, we briefly address both types of proposals along with mentioning major design changes in them. Architecture proposals have addressed mostly by separating the access networks from the core networks. We explain architectural proposals on the core networks first followed by the access networks.

5.3.1 WSDN Architecture on LTE Existing Core Networks SDN based LTE/EPC Architecture Authors in [MRS2014] have proposed an OpenFlow based LTE Evolved Packet Core (EPC) architecture where a new control plane is introduced in WSDN LTE architecture. The control protocols that run on S1-MME between evolve base station (eNB) and mobility management entity (MME) as well as S11 interface between MME and SGW are replaced by OpenFlow protocols. All control functions are separated out of the data forwarding functions in SGWs of the same pool area. The whole SGW intelligence (SGW-C) is shifted centrally and runs on top of the OpenFlow controller as just an application. The data processing is performed by the SGW data plane (SGW-D). Further, they considered converting the MME software as application that runs also on top of the controller. The controller manages the forw arding plane functionalities at eNB and SGW-D using OpenFlow protocols. Figure 5.3 shows the proposed architecture in [MRS2014]. The eNB is not modified and keeps the same radio functions as specified by 3GPP standards; but it is made enabled with OpenFlow protocol for data plane management. Other entities such as PGW and Home Subscriber Systems (HSS) have the same functions as specified by the 3GPP standards.


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SDN based EPC

SGW-C

MME Northbound Interface

OpenFlow Controller

(G S5


TP -C)

OpenFlow Protocol

PGW-C

MacroBS

SGW-U

S1 (GTP-U)

S5 (GTP-U)

PGW-U

UE

Figure 5.3 OpenFlow based LTE/EPC architecture. A number of architecture have been proposed in [JCO2014] based on the integration of SDN with the logical core network entities, for example MME, SGW, and PGW. SDN can be integrated as part of MME for more aware of mobility requirement; or it can be integrated as part of SGW and PGW to control transport network. Option 01: Decoupling SGW and PGW in logical and data plane The logical parts of SGW and PGW are separated and are integrated with the SDN controller that manages the data planes of SGW and PGW. MME interacts with the logical parts of SGW and PGW. The rest of the elements in the network are kept unchanged (Figure 5.4 [JCO2014]). SDN based control and data planes separation for SGW and PGW Policy Control and Charging Rules Function (PCRF)

Home Subscriber Server (HSS)

MME

S11

SGW-C

S5 (GTP-C)

PGW-C

Northbound Interface S1-M ME

SDN Controller

Southbound Interface (e.g., OpenFlow Protocol)

MacroBS

S1 (GTP-U)

SGW-U

S5 (GTP-U)

PGW-U

UE

User plane (U-plane) Control plane (C-plane)

Figure 5.4 Integration of SDN with S/P-GW.


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Option 02: Linking SDN controller with the MME In this approach, the SDN controller can receive mobility events directly from the MME and can optimize the routing paths applying new rules in the switching nodes (Figure 5.5 [JCO2014]). SDN Controller Integrated with MME Policy Control and Charging Rules Function (PCRF)


MME

S1-M ME

No rt Int hbou erf nd ace

SDN Controller Southbound Interface (OpenFlow Protocol)

MacroBS

OpenFlow Switch

S1 (GTP-U)

OpenFlow Switch

SGW-U

S5 (GTP-U)

PGW-U

UE


Figure 5.5 Integration of SDN with MME. Option 03: Integrating controller with SGW, PGW and MME functionalities In this option, the functionalities of both SGW and PGW along with MME are integrated with the SDN controller in the same network element. This results in disappear of SGW and PGW elements exist in the current LTE core network and are replaced by SDN based switch such as OpenFlow switch for data processing. This option needs major modifications in the existing core networks – a disruptive solution. However, it can enable optimizing the data plane with high speed and flow level processing with OpenFlow and support for gradual introduction of high network throughputs and optimal flow management and traffic engineering possibilities. Figure 5.6 [JCO2014] shows this architecture. SDN Controller Integrated with SGW, PGW and MME Policy Control and Charging Rules Function (PCRF)


S1-M ME

MME

No rt Int hbou erf nd ace

SDN Controller

Southbound Interface (OpenFlow Protocol)

MacroBS

S1 (GTP-U)

OpenFlow Switch

UE


Figure 5.6 Disruptive integration of SDN with MME.


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5.3.2 WSDN Architecture with Change on Existing LTE Core Networks Softcell Softcell architecture as shown in Figure 5.7 [XJI2013] considers changing in the current LTE core networks by removing current specialized network elements such as SGW, PGW and point-to-point tunneling. Instead, it considers supporting stateful middleboxes such that all packets in both directions of a connection must traverse the same instance. The controller implements high level service policy to direct traffic through middleboxes by using switch level rules. Each base station is associated with an access switch that is responsible for fine-grained packet classification on UE’s traffic. The rest of the network consists of core switches, including a few gateway switches connected to the Internet. The core switches are responsible for traffic forwarding functions through appropriate middleboxes [XJI2013]. Softcell provides numerous fine-grained policies in a scalable manner in the core network. The controller directs traffic over network and middlebox paths based on service policy, abstracted at high level based on subscriber attributes and applications. A service policy includes multiple clauses that specify which traffic should be handled in what way. A traffic is specified by a Boolean expression on subscriber attributes and applications types, and an action, (i.e., the way) is speci fied by a sequence of middleboxes to be used, along with specifying the quality-of-service and access control for forwarding a traffic [XJI2013]. An example service policy clause is: VoIP traffic must go through an echo canceller first followed by a firewall.

Operator’s Input commands: policy, goal, etc.

Applications


SDN Controller


Transcoder

MacroBS UE 1

Core switch

Core switch

Access switch

Gateway access switch

Internet MacroBS UE 2

MacroBS UE 3

Core switch

Access switch

Transcoder Data flow for UE 2

Core switch

Access switch

Firewalls

Transcoder Middleboxes

Radio Access Networks

Core Networks

Figure 5.7 Softcell network architecture.


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5.3.3 WSDN Architecture on Existing LTE Access Networks SoftRAN SoftRAN is a result of the application of SDN principle on the LTE radio access network. SoftRAN is a software defined centralized control plane for access networks that abstracts all physical base stations in a geographical area as a virtual big base station that includes a controller and radio elements. Rather than controlling radio resources of each base station by itself in a distributed manner, all resources are allocated by a central controller among neighboring base stations. Radio resources are abstracted in three dimensions: space (base station identifier), time, and frequency and are programmed by a logically centralized controller. In each time-frequency block, the controller is to make a decision that is conveyed to each base station and assigns appropriate transmitted power and the flow to be served by the base station. As shown in Figure 5.8 [AGU2013], a centralized controller receives local network state periodically from all base stations in a local geographical area.

Operator’s Input commands: policy, goal, etc.

Applications


SDN Controller

Periodic Updates from Base Stations

Controller API

RAN Information Base Flow Records

Interference Map

Transmitted Bytes

Transmitted Bytes

Number of Received Number of Received Flow RecordsPackets in Queue Packets in Queue Rate

Rate

Resource Grid with Allocations

Radio Bearer Control Radio Admission Control Dynamic Resource Allocation Packet Scheduling Mobility Control Load Balancing etc.

Frequency

Ti

me

Power Allocation Flow Allocation

Network Operator Preferences

Radio Resource Management Modules

Base Station

Allocated Radio Resources to Base Stations APIs

Subscriber Information


MacroBS UE 1

Base Station API

Access switch

MacroBS UE 2

MacroBS

Base Station API

Access switch

Base Station API

UE 3

Figure 5.8 SoftRAN architecture.


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The controller then updates and maintains the global network state in the form of a database called RAN Information Base (RIB) that contains information on interference map (in the form of weighted graph), flow records (in the form of number of bytes transmitted, number of packets queued, etc.), and network operator preference to provide priority service. The RIB is accessed by various control modules to take decisions on radio resource management.

5.3.4 WSDN Architecture on WiFi/WiMAX Networks OpenFlow Wireless OpenFlow Wireless is a mobile wireless platform for experimental network research and realistic deployments of networks and services using virtualization as shown in Figure 5.9 [KYA2010]. OpenFlow Wireless uses OpenFlow protocol to separate the control plane from the underlying data path. A network hypervisor called FlowVisor is used to virtualize the data plane to create network slices and to provide isolation between slices such that mu ltiple experiments can co-exist and run in parallel with the production network without any intervention. In addition, a SNMPVisor is also used to configure radio specific problems [KYA2010]. All control and management applications communicate with the co ntroller with a standard interface, and their decisions are conveyed to the data path by OpenFlow protocol. The detail on testbed implementation of OpenFlow Wireless is provided in [KYA2009].

OpenFlow Wireless Platform

Functionalities

Applications Authentication, Authorization, and Accounting (AAA)

Mobility Manager

Routing

...

Control and Management Applications

Control Plane


OpenRoads FlowVisor

SNMPVisor

OpenFlow

SNMP

Northbound Interface Functionalities SDN Controller

Resource Virtualization and Radio Specific Functionalities Southbound Interface Functionalities

Separation

WiFi Access Point

OpenFlow Switches

Base Station

Data Plane

DataPath

Data Forwarding Functionalities

WiFi Access Point

Base Station

Figure 5.9 OpenFlow Wireless architecture.


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5.4 WSDN Implementation Requirement and Challenge Even though the adoption of SDN is highly desirable in wireless networks, there are several challenges that are be addressed for the viability of WSDN. From research experimentation through technology development to policy level, in all aspects a number of challenges that need to be addressed and solved for the deployment of WSDN. Few key challenges and requirements for WSDN implementation are outlined that may need im mediate attention. 1)

SDN was developed keeping wired networks in mind. But, characteristics of wireless medium is far different and unpredictable than its counterpart wired networks. Adopting the wireless medium characteristics in SDN is very challenging and needs considerable researches on this issue.

2)

SDN is now in its very early stage, and hence most elements of SDN are under developing or not available in commercial markets for research experimentation. For example, the popular OpenFlow protocol speci fication is under developing phase and still does not provide all radio configuration related functionalities – an important aspect of wireless resource management.

3)

SDN has so far been proposed to be integrated with the current 4 th generation (4G) mobile architectures without major changes on the current networks [JCO2014]. However, full advantage of SDN adoption can be achieved only when complete SDN solutions can be applied, e.g., deploying high speed data processing switch such as OpenFlow switch.

4)

The current LTE network has just been deployed worldwide, and hence network operators may not be keen much for adopting a new technology - SDN.

5)

SDN is not about performance improvement; it is about new way of networking that provides network scalability and flexibility features. As long as the user demand can be served with the existing network capacity and vendor specific solution, the network operators may not want to invest on the application of SDN to current networks.

6)

Technology-specific bindings of virtualization are important due to the need to preserve efficiency in the unpredictable multi-user multi-accessed wireless medium [HWE2014]. There is not one single dominant wireless technology, but a few major ones exist in wireless networks. And it is also important to understand that not all these technologies benefit equally from various wireless virtualization perspectives [HWE2014]. The benefits of virtualization are most apparent in technologies where the supported bandwidth and the supported number of users are relatively high, leaving enough room for dynamic sharing of resources, such as in 802.11 WLAN and cellular networks.

7)

The network hypervisor such as FlowVisor is also under developing that enables SDN networks with virtualization capability. The current FlowVisor implementation virtualizes only switch forwarding logic, traffic flow space, and associated hardware resources. However, still many resources exist in the network that need to be virtualized such as address space, links, and device configuration [RSH2009].

8)

OpenFlow does not provide any feature to configure data path elements, e.g., transmit power management, ch annel allocation, etc. For that, it is to be relied on command line interface - SNMP or NetConf. For example, OpenFlow Wireless testbed employs SNMPVisor to slice the configuration by watching SNMP messages and sends them to the correct data paths. But, sometimes it is hard or even impossible to slice configuration, e.g., setting power levels for different slices that use the same WiFi access point (AP). Hence, if a channel is shared by several slices, and we want to set different power levels for each slice’s flow, is not possible on existing WiFi APs [KYA2010]. So, efficien t wireless resource allocation and interference management cannot be achieved without more enhancements in the radio hardware.

9)

The effectiveness of WiMAX visualization suffers from the lack of coupling with the real scheduler inside the physical base station that results in limitation of scheduler responses which provides only a coarse isolation between different slices.


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10) In WiFi AP based networks, instead of using pure TDMA slicing that is susceptible to tight synchronization between APs, virtual WiFi has been proposed in many literatures. However, creating virtual WiFi is a complicated process since the virtual WiFi architecture is divided into four main components: the guest machine wireless network interface card (NIC) driver, the virtual Wi-Fi device model, the virtualization-augmented device driver and the virtualizationaugmented NIC [HWE2014]. 11) There is not much consensus from 3GPP LTE regarding adopting SDN to LTE networks. Ho wever, in the case of 3G networks, there is already support for the concept of network sharing [NES2013]. 12) Carrying out WSDN research experimentation on real tested is extremely limited or impossible. The reason is threefold. First, implementing a large network such as LTE for WSDN research experimentation is impossible because of huge investment. Second, network operators, e.g. LTE operators usually do not allow external researchers to perform any experimentation to avoid multiple issues, e.g., privacy, security, malfunction, etc. Third, even to implement small scale networks such as Stanford OpenWireless campus networks, needs to address several challenges as we have already mentioned in this section. Specifically, a fully functional network hypervisor, wireless enabled data path hardware switches, update to the existing OpenFlow protocol specification for wireless specific characteristi cs inclusion, properly virtualized WiFi APs, sophisticated NOS with appropriate north bound interface, etc. are to be addressed for WSDN testbed implementations to carry out network research experimentation. 13) To address the need for network research testbed, several research projects such as GENI, ORBIT have been initiated that provide researchers to carry out experimentation using a specific slice in the testbed. However, coordinating with these test beds at far distance is not easy. Moreover, each of these testbed has certain design bounds, for example, ORBIT allows for WiFi technology based experimentation. Hence, anyon e wants to carry out experiments on LTE mobile technology, this cannot be possible with ORBIT testbed. 14) Even though all wireless technologies have one common feature - they all transmit through wireless medium, each technology has certain characteristics which are completely different from another, for example, allowing high UE speed with no performance degradation. Hence, advanced techniques such as virtualization cannot be the same for all wireless technologies, and the most advantage from virtualization can only be achieved when technology specific full fledge of supports and resources are available for implementation. Otherwise, we may limit ourselves drawing results on one technology by carrying out experimentation on it, and leaving assumptions and co mments for others.

5.5 Summary In this chapter, we have presented a new paradigm of networking called software defined networking (SDN) in wireless networks. We have provided a brief on SDN concept and its benefits and generic architecture. We then have presented several existing architectural research proposals on how SDN can be applied in wireless networks called WSDN. We have considered LTE, WiFi, and WiMAX wireless networks and have discussed the application of SDN on these wireless technologies considering both the commonality and the difference among them. Major WSDN implementation challenges and requirements have been outlined elaborately with relevant explanations and example scenarios to provide an insight into the implementation specific issues of WSDN to be considered. This chapter would be useful for startup research ers to consider WSDN as research area and both industry and academic researchers to update many significant insights on WSDN.

CHAPTER 6 RADIO RESOURCE MANAGEMENT IN HETNETS © RONY KUMER SAHA AND CHAODIT ASWAKUL

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6.1 Introduction Radio Resource management (RRM) plays a significant role on overall performance of wireless HetNets such as in LTEAdvanced and its evolution. RRM aims to ensuring efficient use of available radio resources of a network while fulfilling the quality of service (QoS) requirements of users. It provides a number of functionalities to manage, e.g., share, assign, re-assign, and release radio resources efficiently, considering both single cell and multi-cell aspects. Among a number of functionalities that LTE RRM provides, in this chapter, we focus on presenting the underlying concepts of the major RRM functionalities, namely radio admission control (RAC), radio bearer control (RBC), connection mobility control (CMC), dynamic resource allocation or packet scheduling, inter-cell interference coordination (ICIC), load balancing, and cooperative multi-point (CoMP). Few of these functionalities will be explained broadly while others will be limitedly included. We will end the chapter with a proposal on considering the development of multi-objective modular based complete RRM solution for LTE Evolution/5G DenseNets.

6.2 RRM Functionalities in LTE Systems 6.2.1 Radio Bearer Control Radio bearer can be defined as a logical transmission path between entities for bearing IP packets [RED2013] with treated with a set of quality-of-service (QoS) parameters that, i.e., how an UE data is to be treated as it travels across the network. Radio bearer control (RBC) is concerned with the establishment, maintenance, and release of radio bearers by configuring radio resources [OVE2014]. It allows a bearer to set up by taking into account of overall resources in the radio access network (RAN), maintains an on-going bearer with changing environments such as mobility, channel state, etc., and releases a radio bearer based on aspects such as session termination, handover, etc. [OVE2014] . Based on the entities between which a bearer works to carry IP packets, bearers can be termed differently. A bearer that carries packets in the radio interface, i.e., user equipment (UE) and Evolved NodeB (eNB) is called radio bearer, and similarly, if so is done between eNB and Serving Gateway (S-GW) is called S1 bearer; between S-GW and Packet Data Network Gateway (P-GW) is called S5/S8 bearer; between UE and S-GW is called Evolved-UTRAN Radio Access Bearer (E-RAB); and between UE and (P-GW) is called Evolved Packet system (EPS) bearer. Hence, radio, S1, and S5/S8, and E-RAB bearers bear the EPS bearer between the corresponding entities as aforementioned. Figure 6.1 [OVE2014] shows LTE EPS bearer hierarchy. E-TRAN

EPC eNB

UE

Internet Peer Entity

P-GW

S-GW

End-to-end Service

EPS Bearer

E-RAB

External Bearer

S5/S8 Bearer

Radio Bearer

S1 Bearer

S1

Radio

S5/S8

Gi

Figure 6.1 LTE EPS bearer hierarchy. An EPS bearer may contain more than one service data flow (SDF). All SDFs under the same EPS bearer experience the same packet forwarding treatment, e.g., scheduling policy. Note that each EPS bearer has certain Quality of Service (QoS) assigned and only those SDFs can maintain the same QoS of EPS bearer, can be mapped to that EPS bearer. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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When an UE is connected with a PDN, an EPS bearer is set up and remains until the session to the PDN is terminated to provide UE an always-on IP connectivity to that PDN. This bearer is referred to as default bearer. If any additional bearer after initial connection establishment to that PDN is established is referred to as dedicated bearer. A dedicated bearer can be either guaranteed bit rate (GBR) bearer or non-GBR bearer. However, default bearer are always non-GBR bearer.

Radio Bearers To provide continuity of connection to the UE, more than one Radio bearer (RB) are initiated, each with different QoS requirements. There two types of LTE radio bearers: signaling radio bearers (SRB) and data radio bearers (DRB). SRBs consists of three types of bearers, namely SRB0, SRB1, and SRB2. SRB0 bears radio resource control (RRC) messages using Common Control Channel (CCCH); SRB1 bears RRC or non-access stratum (NAS) messages using dedicated control channel (DCCH) logical channel and is used prior to the establishment of SRB2; and SRB2 bears NAS messages using DCCH logical channel and has lower priority than SRB1. However, DRBs carry only user plane content on the radio air interface. Bearer are mapped based on one-to-one mapping between bearers, e.g., DRBs are mapped to and from S1 bearers and are also mapped to uplink packet filters in the uplink; S1 bearers are mapped to and from S5/S8 bearers; and S5/S8 bearers are mapped to downlink packet filters. These mapping are stored respectively in eNB and UE, S-GW, and P-GW. Note that SRB non access stratum (NAS) messages are mapped to stream control transmission protocol (SCTP) association.

Service Data Flow A Service Data Flow (SDF) filter is a set of packet flow header parameter values used to identify one or more of the packet flows that constitute an SDF. The set of SDF filters in a Policy and charging control (PCC) rule forms an SDF template. SDF is an aggregate set of flows that matches an SDF template [RED2013]. Hence, an IP flow or an aggregate of IP flows of user traffic classified by using SDF template. Different QoS is applied to each SDF, and each SDF is delivered through an EPS bearer that can satisfy its QoS. An EPS bearer transports one or more SDFs to provide connectivity between a UE and a PDN. Note that multiple SDFs with the same QoS class Identifier (QCI) are mapped and delivered to one EPS bearer. SDF filter is based on packet header characteristics which may consider for example IP header only (source and destination IP address, protocol Id, traffic class, etc. Figure 6.2 [RED2013] shows an example of how SDFs are mapped to EPS bearers. EPS bearer is the concatenation of DRB, S1 bearer, and S5 bearer. Each EPS bearer is activated with certain QoS parameters that characteriz e the transport path. All EPS bearers belonging to on PDN connection share the same UE IP address. An LTE network identifies a PDN by its access point name (APN) and assigns a PDN address (IP address) to the UE. A PDN connection is also referred to as EPS session that provides IP connection between UE and PDN. An EPS session at least has an EPS bearer and remains activated as long as UE is connected to the PDN. IP flows 1 and 2 are mapped to SDF 1 through downlink packet filter. Similarly IP flows 3 is mapped to SDF 2 and IP flows 4 is mapped to SDF 3. Based on the QoS requirement of SDFs, SDFs 1 is mapped to default EPS bearer, whereas SDFs 2 and 3 are mapped to dedicated EPS bearer. These EPS bearers are now responsible to carry the IP flows with proper QoS enforcement at different nodes, e.g., S-GW, eNB and to the UE.


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eNB

UE

PDN

P-GW

S-GW

IP address

SDF 1 QoS Policy

Default EPS Bearer

SDF 2 QoS Policy

Dedicated EPS Bearer

SDF 3 QoS Policy

S1

Radio

S5/S8

IP Flow 1

SDF Template

Applications

APN

IP Flow 2 IP Flow 3 IP Flow 4

Gi

Figure 6.2 An example of SDF’s mapping to EPS bearers.

Bearer QoS Parameters The bearer level e.g., per bearer or per bearer aggregate QoS parameters are QCI, ARP, GBR, and AMBR [OVE2014]. Each EPS bearer whether GBR or Non-GBR is associated with the bearer level QoS parameter such as QoS Class Identifier (QCI) and Allocation and Retention Priority (ARP). On the radio interface, each RLC Packet Data Unit (RLC PDU) is indirectly associated with one QCI via the bearer Identifier. QCI A QoS Class Identifier (QCI) is a scalar that is used as a reference to access node-specific parameters controlling bearer level packet forwarding treatment, e.g. scheduling weights, admission thresholds, and that have been pre-configured by the operator owning the eNB [OVE2014]. The standardized characteristics associated with standardized QCI values are defined in terms of performance characteristics such as resource type (GBR or Non-GBR), priority, packet delay budget, and packet error loss rate [OVE2014]. More detail on QCI can be found in [OVE2014]. The resource type determines if dedicated network resources related to a service or bearer level Guaranteed Bit Rate (GBR) value are permanently allocated. The Packet Delay Budget (PDB) defines the maximum time that a packet may be delayed between the UE and the PCEF to support the configuration of scheduling and link layer functions and is interpreted as a maximum delay with a confiden ce level of 98 percent. The Packet Error Loss Rate (PELR) defines the maximum rate of SDUs that have been processed by the sender of a lin k layer protocol but that are not successfully delivered by the corresponding receiver to the upper layer (e.g. PDCP in E-UTRAN) [OVE2014] to allow for appropriate link layer protocol configurations (e.g. RLC and HARQ in E -UTRAN). Note that the characteristics PDB and PELR are specified only based on either application, service, or bearer level requirements and hence should be regarded as being independent from the roaming scenario as well as from the operator policies. ARP The ARP means Priority of Allocation and Retention [OVE2014] that contains information about the priority level (scalar), the pre-emption capability (flag) and the pre-emption vulnerability (flag) to decide whether a bearer establishment or modification request can be accepted or needs to be rejected due to resource limitations. The priority level information is used to ensure that a bearer request with the higher priority level is preferred and to decide which bearer(s) to drop during resource scarcity period, if needed; the pre-emption capability defines whether a bearer with a lower ARP priority level should be dropped to free up the required resources; and pre-emption vulnerability information defines whether a bearer © RONY KUMER SAHA AND CHAODIT ASWAKUL

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is applicable for such dropping by a pre-emption capable bearer with a higher ARP priority value. In both cases, the decision can be either yes or no. An example use case is when Video telephony where it may be beneficial to use EPS bearers with different ARP values for the same UE. In this use case an operator could map voice to one bearer with a higher ARP, and video to another bearer with a lower ARP. In a congestion situation (e.g. cell edge) the eNB can then drop the "video bearer" without affecting the "voice bearer". This would improve service continuity. The range of the ARP priority level is 1 to 15 with 1 as the highest level of priority. The ARP priority levels 1-8 is assigned for prioritized services within an operator domain, i.e. by the serving network, and priority levels 9-15 can be applicable when a UE is roaming. GBR and MBR Guaranteed Bit Rate (GBR) defines the bit rate that can be expected to be provided by a GBR bearer, and Maximum Bit Rate (MBR) limits the bit rate that can be expected to be provided by a GBR bearer. APN-AMBR Each Access Point Name (APN) access by a UE is associated with the QoS parameter: APN Aggregate Maximum Bit Rate (APN-AMBR) which is a subscription parameter stored per APN in the HSS to limit the aggregate bit rate that can be expected to be provided across all Non-GBR bearers and across all PDN connections of the same APN. The P-GW is responsible for enforcing the APN-AMBR in downlink, and in the uplink is performed by the UE and additionally in the P-GW [OVE2014]. UE-AMBR Each UE in state EMM-REGISTERED is associated with the bearer aggregate level QoS parameter: UE Aggregate Maximum Bit Rate (UE-AMBR) which is limited by a subscription parameter stored in the HSS to limit the aggregate bit rate that can be expected to be provided across all Non-GBR bearers of a UE (e.g. excess traffic may get discarded by a rate shaping function). Note that GBR bearers are not involved with UE-AMBR and enforcement of the UE-AMBR in uplink and downlink is done by eNB. Note that the GBR and MBR denote bit rates of traffic per bearer while UE-AMBR/APNAMBR denote bit rates of traffic per group of bearers. HSS The Home Subscriber Server (HSS) is responsible for defining, based on each PDN subscription context, the bearer level QoS parameter values for the default bearer (QCI and ARP) and the subscribed APN-AMBR value to set the priority level of the EPS bearer parameter ARP for the default bearer.

6.2.2 Connection Mobility Control Connection mobility control (CMC) is concerned with the management of radio resources in connection with UE idle or UE connected mode mobility. In UE idle mode, the cell reselection algorithms are controlled by setting of parameters such as threshold value to define the best cell and (or) to determine when the UE should select a new cell. In UE connected mode, the mobility of radio connections are to be supported and handover decisions can be based on UE, eNB measurements, neighbor cell load, traffic distribution, transport and hardware resources, and operator defined policies [OVE2014]. CMC is located in the eNB. Hence, the mobility of an UE is controlled by based on whether the mobile is in Idle or connected mode. In RRC_IDLE mode, the mobility is concerned with the maximization of UE’s battery life and the minimization of the network’s load and is triggered by the UE using cell reselection procedure. However, in RRC_CONNECTED mode, the mobility of an UE is concerned with the handover from one cell to another based on UE’s activity, and is triggered by the network based on measurement procedures to decide on whether or not the UE is to be handed over to another cell.


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In RRC_IDLE mode, to save the battery power, the UE wakes up in every discontinuous reception cycle to monitor the paging message and to make measurements, to minimize the signaling load the UE performs cell reselection procedure itself to decide whether or not to move to a new cell [CCO2012]. For single carrier LTE, the cell reselection is done by measuring the reference signal received power (RSRP) of the serving cell as long as the following equation satisfies [CCO2012],

S rxlev  S IntraSearchP

(6.1)

where S IntraSearchP is the threshold of the serving cell, and S rxlev depend on the RSRP of the serving cell calculated by the following equation.

Srxlev  Qrxlevmeas  Qrxlev min  Pcompensation (6.2) where Qrxlevmeas is the cell’s reference signal received power (RSRP), defined as the average power per resource element. Qrxlevmin is a minimum value for the RSRP, and Pcompensation is a parameter that makes sure that an UE only selects the cell if the base station can hear it on the uplink and is calculated as follows:

Pcompensation  maxPEMAX  PPowerClass , 0 (6.3) where, PEMAX is the BS allowable maximum UE transmit power, and PPowerClass is the intrinsic maximum power of the UE. If the equation 6.1 satisfies, the UE then starts measuring the neighboring cells, including CSG HeNBs in HetNets. Once the UE finds a suitable cell, it computes the followings [CCO2012]:

Rs  Qmeas , s  Qhys Rn  Qmeas , n  Qoffset, s , n

(6.4)

where Rs and Rn are the ranking scores of the serving cell and one of its neighbors. Qmeas, s and Qmeas, n are the respective RSRPs. Qhyst is a hysteresis parameter to help the UE not to bounce back and forth between cells with the fluctuation of the signal levels. Qoffset , s, n is an optional cell-specific offset to encourage or discourage the UE to or from individual neighbors. If the HetNet contains any CSG cells, the UE follows the same procedure to camp on the CSG cell, provided that the UE is also the member of the CSG cell. The UE can camp on to the new cell only when the following three conditions are satisfied: (1) the UE must spent on the serving cell for at least one second, (2) the new cell must be suitable based on the suitability conditions as explained, and (3) the new cell must be ranked better than the serving cell for a time of T reselection ,EUTRA, which is usually 0 to 7 seconds [CCO2012]. From release LTE release 9 onwards, an UE can also start measuring the neighboring cells if the following equation satisfies:

Squal  S IntraSearchQ (6.5) where, S IntraSearchQ is a threshold and S qual depends on the serving cell’s reference signal received quality (RSRQ) and is calculated as follows.

Squal  Qqualmeas  Qqual min (6.6) © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Where Qqualmeas is the reference signal received quality (RSRQ), i.e., the reference signal-to- interference-plus-noise ratio, and Qqualmin is a minimum value for the RSRQ. This additional feature helps the UE not to camp on a neighboring cell with interferen ce levels. However, if the LTE network operates on more than one frequency, which is the usual case for HetNets, the serving cell advertises the other carriers and prioritize each carrier with a priority level from 0 to 7, where 7 is the highest priority along with its own carrier priority level [CCO2012]. The priority levels are mostly significant in HetNets where more than one carrier frequency can be considered, for example, small cells operate at one frequency and the macrocell operate on another carrier, usually with higher priority to provide more coverage and service continuity with less hand -offs. For multi-carrier scenario, measurement initiations and cell reselection depend on the priority levels, i.e., whether or not the new cell has the higher, the same or the lower priory than the serving cell. For a new cell with carrier priority higher than the serving cell, the UE camps on to the new cell regardless of the new cell’s signal quality which is measured by the UE in the discontinuous reception cycle separately. The UE moves to a new cell when three conditions satisfies: the UE has spent at least 1 second in the serving cell, the new cell is suitable, and t he new cell’s RSRP meet the following condition for at least time, T reselection,EUTRA:

S rxlev, x, n  Threshx, HighP (6.7) where, Thresh x, HighP is a threshold for frequency x, S rxlev, x, n depends on the new cell’s RSRP calculated as in the case for single carrier LTE scenario. Note that there is no need for the measurement of the serving cell RSRP. For a new cell with carrier priority the same as the serving cell, the UE starts measuring the new cell when the following condition is satisfied:

S rxlev  S NonIntraSearchP (6.8) where, S NonIntraSearchP is a threshold, and S rxlev depends on the serving cell’s RSRP as before. The UE reselects the new cell following the same procedure as in the case for single carrier LTE scenario already described, however, on top of that the serving cell cann add optionally a frequency-specific offset Qoffset, frequency . For a new cell with carrier priority lower than the serving cell, the UE starts measuring the new cell using the same criterio n as for an equal priority frequency, and moves to a new cell on that frequency if several conditions are satisfied, e.g., the UE have spent at least 1 second in the serving cell, the new cell is suitable, and the UE is unable to find a cell on it serving cell frequency, or on a frequency with an equal or higher priority to camp on. In addition, the RSRPs of the serving and neighboring cells should satisfy the following conditions, for a time of at least T reselection ,EUTRA [CCO2012]:

S rxlev  ThreshServing , LowP S rxlev , x , n  Threshx , LowP

(6.9)

where Thresh Serving, LowP and Thresh x, LowP are thresholds, and S rxlev and S rxlev, x, n depend on the RSRP of the serving and neighboring cells. For CSG cells, if a CSG UE detects a suitable CSG cell with highest priority on another carrier, the UE moves on to that CSG cell, provided that the UE’s serving cell is non-CSG category, regardless of the priority of the new carrier frequency. However, this is not applicable for CSG on the same carrier frequency [CCO2012]. Two new features are included in Release 9 for multi-carrier LTE scenario: the UE can start measurements of neighboring cells on an equal or lower priority frequency if the RSRQ falls below the following threshold:

S qual  S NonIntraSearchQ (6.10) And the BS can optionally replace the criteria for a new cell selection on a higher or lower priority frequency, with similar criteria based on the RSRQ than on the RSRP.


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The above discussion is based on normal UE case where static T reselection ,EUTRA is considered. Few adjustment are to be considered when UE is on the move since static T reselection,EUTRA causes unwanted delay in the cell reselection procedure and can make the procedure inefficient. To address the mobility issue, the UE can measure the rate at which it is making cell reselection, and based on that can determine its states: normal, medium or high mobility. For medium and high mobility states, the UE can reduce the value of T reselection ,EUTRA, using a scaling factor between 0.25 and 1 [CCO2012]. This can help reduce the delay in the procedure and camp on faster to the new cell. In addition, the UE can reduce also the hysteresis parameter Qhyst , mentioned earlier, to help the UE be less stick to the sering cell and make easy the cell reselectio n procedure. In RRC_CONNECTED and ECM-CONNECTED states, since the UE is active and transmit and receives data to (from) the network, based on the UE measurement reports, the BS can initiate a handover request to a new cell. The measurement report can be periodic from 120 ms to 60 minutes, and are usually triggered by a number of measurement events happen when the signal levels cross over the predefined thresholds [CCO2012]. For example, if a neighboring LTE cell signal rises above the serving plus the offset, the measurement report is sent to the network when the following condition is satisfied.

M n  Of n  Ocn  M s  Of s  Ocs  Off  Hys

(6.11)

The UE do not report further until the following condition is satisfied.

M n  Of n  Ocn  M s  Of s  Ocs  Off  Hys (6.12) where Ms and Mn are the signal measurements of UE’s of the serving and neighboring cells respectively. Hys is a hysteresis parameter that ensure not to report any further as long as the signal level is changed within 2 times the Hys centering at the Off , which is also a hysteresis parameter for handovers. Off help prevent the UE from moving back to the original cell s long as the signal change is within 2 times the Off. Optional parameters are frequency-specific offsets Ofs and Ofn and cell-specific offsets Ocs and Ocn .

Difference in measurement between servingand neighboring cell (Mn - Ms)

Figure 6.3 [CCO2012] shows Operation of measurement for the event described where Of s, Ofn , Ocs, and Ocn are considered zero. The UE sends a measurement report to the BS only when the difference between the measurements of the neighboring and serving cells exceeds Off + Hys and does not report as long it is below Off − Hys. Measurement Reports for the event

Off + Hys Off

2 x Hys

Off - Hys

Time

Figure 6.3 Operation of measurement and reporting for an event. Note that if a neighboring cell operates on a different carrier frequency from the serving eNB, then assuming that the UE does not have expensive dual frequency receiver, the BS can define measurement gaps (Figure 6.4) [CCO2012] in terms of subframes in which the BS does not schedule any uplink or downlink traffic to the UE. Hence, the UE can make measurement at different frequency during the measurement gaps, each with gap length of six subframes and repetition period of either 40 or 80 subframes [CCO2012].


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Measurement gap repetition period (40 subframes )

Measurement gap length

Frame (10 ms)

Measurement gap length

Subframe (1 ms)

Figure 6.4 Measurement gaps in RRC_CONNECTED mode.

Mobility Procedure The mobility procedures involve the network interfaces such as X2 and S1 interfaces. In LTE, handover signaling procedures support both, inter-eNB handover and inter-RAT handover. Inter-RAT handovers is initiated via the S1 interface and Inter-eNB handovers is initiated via the X2 interface except if any of the following conditions are true, in that hand over for Inter-eNB handovers is initiated via the S1 interface [OVE2014]. 1. The source eNB is not an RN, and there is no X2 between source and target eNB. 2. The source eNB is an RN, and there is no X2 between DeNB and the target eNB, or between the source RN and the DeNB. 3. The source eNB is an RN, and the UE’s serving MME is not included in the MME Pool(s) connected with the target eNB. 4. The source eNB has been configured to initiate handover to the particular target eNB via S1 interface in order to enable the change of an EPC node (MME and (or) Serving GW). 5. The source eNB has attempted to start the inter-eNB hand over via X2, but receives a negative reply from the target eNB with a specific cause value. Intra-LTE Mobility over S1 The overall Intra-LTE S1-handover procedure consists of a preparation phase to prepare first the resources at the target side, followed by an execution phase and a completion phase. An addition compared to UMTS, is the ‘STATUS TRANSFER’ message sent by the source eNB to carry some PDCP status information needed at the target eNB when the PDCP status preservation applies for the S1-handover to make alignment with the handover procedure on X2 in which this information is sent within the X2 ‘STATUS TRANSFER’ message. Hence, the handling of the handover by the target eNB as seen from the UE is the same, regardless of the handover performed on S1 or X2 interface. For mobility towards small cells such as FemtoBSs or HeNBs involves additional functions from the source macroBS and the MME. Along with the E-UTRAN Cell Global Identifier (ECGI), the source macroBS includes the Closed Subscriber Group Identity (CSG ID), and the access mode of the target HeNB in the ‘HANDOVER REQUIRED’ message to the MME to help MME perform the access control to that HeNB. For the target HeNB operates in CSG mode, and the MME fails the access control, the MME usually rejects the handover, otherwise the MME accepts and continues the handover along with indicating to the target HeNB whether the UE is a ‘CSG member’ if the HeNB is operating in hybrid mode. Intra-LTE Mobility over X2 Like the S1-handover, mobility over X2 is also composed of a preparation phase, an execution phase and a completion phase for intra-eNB handover. However, for mobility towards small cells such as HeNBs via X2, In Release 10 of LTE, there is no necessity that the mobility between two HeNBs needs to use S1 handover via the MME, rather it can use X2 handover directly. However, X2 handover between two HeNBs is only allowed for cases where the MME does not need to perform access control, i.e., when the source and target HeNBs are either in closed or in hybrid access modes and have the same CSG ID, or when the target HeNB is in open access mode. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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6.2.3 Load Balancing Introduction Load balancing is a process where network elements share ongoing traffics to one another such that the overall load of the network is distributed uniformly between elements. In LTE, using inter-cell handover, between macroBSs over X2 can be accomplished. Also using intra-cell handover, in HetNets, traffic from the macrocell can be offloaded to the small cells. In existing networks, parameters are manually adjusted to obtain a high level of network operational performance. However, in LTE, the concept of self-optimizing networks (SON) is introduced where the parameter tuning is done automatically based on measurements. Load Balancing (LB) as a functional element of SON can provide extra gain in network performance by adjusting the network control parameters in such a way that overloaded cells can offload the excess traffic to low loaded adjacent cells, whenever available. In a live network high load fluctuations occurs and they are usually accounted for by over dimensioning the network during planning phase [ALO2010]. For example in [ALO2010], the load balancing algorithm aims at finding the optimum handover (HO) offset value between the overloaded cell and a possible target cell. This optimized offset value will assure that the users that are handed over to the target cell will not be returned to the source cell and thus the load in the current cell is diminished. Load balancing is a key aspect in radio resource management. For UMTS, load balancing, e.g., by adjusting pilot power levels, has been extensively [ISD2012]. For HetNet deployment, the topic is much more challenging, because of the asymmetric interference relation between the macro-cells and LPNs. To avoid overloading and radio resource exhaustion, approaches for effective load balancing are essential.

Load Balancing Operations Load balancing (LB) is done by the means or handover actions or self-optimization of mobility parameters [OVE2014]. Support for mobility load balancing consists of one or more of the functions such as load reporting, load balancing action based on handovers and adapting handover and (or) reselection configuration to balance the load. Load reporting, or load information exchange, enables eNBs to exchange information about their load level in cells and about their available capacities. For LTE, the reporting is initialized by one eNB to another eNB with the Resource Status Reporting Initiation X2 procedure. Addressing each of these functions is optional and implementation specific. The functional architectu re for LB is shown in Figure 6.5 [OVE2014].

O&M

Load balancing algorithm

Load reporting function

HO (cause load balancing)

Adapting HO parameters

Support for mobility load balancing

RAN

Figure 6.5 Functional architecture of SON load balancing. © RONY KUMER SAHA AND CHAODIT ASWAKUL

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Load Balancing in LTE HetNets Heterogeneous networks, in short HetNet, is one of the promising features in LTE -Advanced system which incorporate small cell deployment overlaying the big one, i.e. macrocell to increase coverage, capacity, and to improve user always-on connectivity experience. However, along with several enhancement features of small cells, raises many issues that need to be properly addressed, for example, load balancing to gain the favor of the enhancement features. Load balancing in HetNet is much more challenging than a homogeneous network because several inherent disparities exist between the cell types in HetNet, for example, transmit power, coverage, load capability, network planning, cost, size, etc. to mention a few. Primarily, small cells (SCs) address the need for the demand of hotspot areas service enhancement where user density is very high and traffic fluctuation because of on-the-move users or short-time static users. Since the transmit power of an SC is relatively much lower than a macrocell (MC), the downlink signal strength received at the UEs from the MC is higher than an SC, and UEs usually get connected to the MC. This creates several issues, and load unbalancing is probably the foremost of all that causes the MC to get overloaded, whereas SCs remain under loaded. Hence, the purpose that drives the deployment of SCs does not work out any more. So, an obvious question gets raised how this unbalancing in load distribution between MCs and SCs in HetNet can be overcome. There are several ways that may encounter this issue, e.g., SC cell range extension (CRE), increasing transmit power of the SCs, reducing transmit power of the MC, adjusting the MC antenna height and azimuth angle, or relocatin g the SCs. However, transmit power changes in not feasible in a sense that it raises interference to the UEs both from the MC and the SCs and also concerns against the cost and the size of the SCs [SLA2011]. And so is adjusting antenna height and azimuth angle since adapting to dynamic load changes is challenging. The same is applicable also for relocating the SCs since it affects the network planning and optimization. CRE seems a good candidate for addressing the load balancing issue through extending the SCs coverage areas virtually so that more UEs can get connected to the SCs. In doing so, a cell selection offset or handover threshold is added to the reference signal received power (RSRP) of the SCs to compensate the transmit power difference between the MC and the SCs during cell selection procedure such that the UEs consider (RSRP+ Offset) than RSRP from the SCs while taking decision on which cells to get connected – the MC or an SC. The offset is chosen to set such that the (RSRP+ Offset) from an SC equals to the RSRP from the MC at a point of interest accord ing to the SC range that needs to be extended, from what is covered without offset. It means that the CRE is proportional to the cell selection offset for the SCs. However, in contrary to the homogeneous networks, in HetNets, since the transmit power of the SCs and the MC is not the same, the uplink and downlink coverage, i.e., the strongest received signals for uplink (SSUL) and downlink (SSDL) from the SCs and from the MC are equal, i.e., coincide usually at different points as shown in Figure 6.6 [JWK2014] This is because of the less pathloss between the UEs and the SCs than between the MC and the UEs in the uplink; and the more SSDL from the MC to the UEs than from the SCs to the UEs in the downlink. Note that RSRP can be used for measuring the strongest received signal at the UEs.

UE Small cell

Macro cell

SSDL-Macro = SSDL-small cell

PLUL-Macro = PLUL-small cell

Figure 6.6 Different uplink and downlink coverage points in HetNet s.


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The cell selection offset is chosen such that the extended range of the SCs is usually within the boundaries between these two points of coincidence for uplink and downlink as shown in Figure 6.7 [JWK2014].

UE Macro cell Extended cell coverage

Small cell SSDL-Macro = SSDL-small cell

SSDL-Macro= SSDL-small cell+offset PLUL-Macro = PLUL-small cell

Figure 6.7 Cell range extension procedure in HetNets. Within the CRE region, the uplink received power (hence, the data rate) at SCs is increased because of the less pathloss from the SCs than the MC to the UEs. However, in the downlink, the UEs served by the SCs are severely interfered by the MC. Some coordination, e.g., ICIC and CoMP between SCs and MC can minimize the effect of interference from the MC to UEs in the CRE region in the downlink. Note that we assume that there is no power control at the UEs, i.e., UE’s transmitting power is fixed. The value of the cell selection offset or threshold can be uniform or non -uniform. When all SCs use the same value of offset, we call it uniform cell selection offset. However, finding an optimal value of offset (optimal cell range extension) that can fit all SCs in HetNet is challenging. A too small offset value will result in offloading more to the MC, while a too high offset value will result in overloading the SCs. This can be improved by considering cell specific or non -uniform offset, i.e., different offsets for different the SCs [WAK2012]. The cell specific offsets can help overcome several inherent issues present in the SCs, e.g., distance between the MC and the SCs (e.g., a small distance causes choosing a high offset value because of higher received MC signal strength at the UEs than the SCs), the local environmental conditions in the SC’s coverage (e.g., densely urban or suburban), and asymmetric user distributions based on location and time in the SCs. Cell load can be determined based on the number of resource blocks (RBs) in a cell. More specifically, it is the ratio of the total number of scheduled RBs to the total number of RBs available in a cell. Hence, the cell load is a positive value and can be varied: less than, equal to, or greater than 1. A cell load value less than 1 means the cell can meet the demand for the current users; a value approximately 1 (e.g., 0.9) means the cell is tending to be congested which may result in some existing user’s service ou tage; and a cell load value greater 1 means the cell is overloaded which results in in-capability of the cell to meet the current user’s demand. An optimization tools can be used to find an appropriate value based on scenarios and requirements. Hence, knowing the cell load value, an appropriate cell selection offset value can be obtained through an adaptive optimization tool to provide an optimal CRE for each SC such that the load between the MC and the overlaid SCs in the LTE HetNets can be balanced through on demand offloading between the MC and the SCs.

6.2.4 Radio Admission Control Radio admission control (RAC) is concerned with the new radio bearer requests - whether a request of a new radio bearer is to admit or to reject. To do so, RAC considers the overall radio resources state in an E -TRAN (eNB), QoS requirements, priority levels and the QoS of on-going bearers. It also considers the QoS requirements of the requested new radio bearer. RAC aims at ensuring the high utilization of the available radio resources and in doing so, it accepts requests of new radio © RONY KUMER SAHA AND CHAODIT ASWAKUL

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bearer as long as the radio resources are available and rejects requests of new radio bearers when the available radio resources cannot accommodate the new requests, in order to guarantee the QoS requirements of the on -going radio bearers [OVE2014]. The task of RAB and RAC differs [ZLU2012] from the fact that RAC is concerned with whether or not the request of a new radio bearer is to be accepted, and once accepted, i.e., admitted into the network, it is now the task of RAB how the establishment, maintenance and release of the radio bearer is to be configured based the QoS associated with each radio bearer. A number of approaches can be applied for the design of admission control algorithm such as guard channel, collaborative, non-collaborative, mobility-based, and pricing-based [DNE2005]. In guard channel approach, a certain percentage of the total resources is kept as reserved to serve the hand-off calls such that call dropping probability because of hand -off is minimized. In collaborative approach, a group of neighboring cells collaborate each other for advance reservation of resources by exchanging information, e.g., via X2 backhaul. However, in some scenarios, such as HetNets, exchangin g information is not always feasible because of significant backhaul overhead. In such scenario, admiss ion control (AC) algorithm use prediction based approach (non-cooperative) such as history of bandwidth usage locally in order to predict the resources in need in near future. In mobility-based approach, UE mobility information, e.g., position and directio n of movement is used to estimate the future resource reservation. In pricing based approach, dynamic pricing policy is used to address the call arrival rate [DNE2005]. In HetNets, different kinds of cells with different performances are present, e.g., macrocell, picocell, femtocell, Relay, and RRH. Each cell has different coverage, surrounding environment, and backhaul connection. Further, in 5G along with static cells, there may be mobile cells existed such as mobile femtocell, mobile relay, D2D communication, Machine type communications, etc. unlike traditional cellular networks, there may be multi-RATs such as WLAN, WiMAX, and LTE co-exist in the same network. Also cooperation among cells and networks will be available to address many important issues such as traffic offloading, interference avoidance, etc. In such a dense HetNets with multi-standard, multi-type, multi-feature, and multi-service profile, it will be certainly a challenging task of how UEs are to be admitted that can satisfy both user- and network side performance requirements. Hence, to address such a scenario, novel approaches to AC is to be developed based on the challenges in future networks, a few of them have been mentioned above. More specifically, the AC algorithm should be able to handle vertical hand-off between RATs, along with traditional horizontal hand-off and new AC features is to be included for mobile cells that work like ad-hoc nature (D2D). Multi-service features with multiple QoS requirements are to be addressed [DNE2005], e.g., for data hungry and low latency applications such real-time streaming video, appropriate cell should be considered based on resource availability; an of what would be - if a macrocell -edge UE is close to a picocell while using a simple voice call served by a macrocell , and if the user switches to a real-time video applications, the UE can be switched to the picocell from the macrocell coverage in order to improve the data rate. Adaptive bandwidth allocation [DNE2005] based on UE’s QoS performance and network state can improve the resource utilization and hence, in multi-service scenario, AC algorithm should consider the bandwidth allocation to an UE adaptively, rather than static manner. Since the future 5G network is based on packet tran smission, the admission to a radio bearer should consider the packet level performance such as packet transmission delay, packet drop ratio, packet retransmission mechanism, etc. so that all existing bearers are served with proper QoS as defined and a new radio bearer request is to be accepted only when including the new bearer, all served bearers can be maintained with QoS requirements. A number of AC algorithms have already been proposed to address HetNets features. [EZT2008] proposed an AC algorithm for HetNets that incorporate cooperation between HetNet systems, e.g., WINNER system, GPRS, UMTS, WLAN 802.11b by shifting traffic between WINNER system and other systems based on the most network suitability for an UE to maximize the number of admitted and in-progress traffics over the RANs along with ensuring their QoS requirements and to admit a new request such that in-progress traffics are not affected. [DNE2005] proposed a call admission control (CAC) architecture for 4G Networks, by dividing the CA C module in two parts: wireless and wired parts. In the wireless part, CAC handles calls due to vertical hand -off from other types of networks and consider the capacity of the systems to ensure optimal resource reservation and admission control. In the wired part handles the packet dropping probability transmitted over the air interface and the packet delay and maintains the wireless users in the wired part at desired QoS performance. Both call level and packet level performance requirements are considered in the wireless part, however, only packet level performance is considered for the wired part.


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6.2.5 Dynamic Resource Allocation - Packet Scheduling Dynamic resource allocation (DRA) or packet scheduling (PS) is one of the most important functionalities of RRM. Almost all other functionalities are affected with the performance of PS. PS allocates or de-allocates resources including buffer and processing resources, resource blocks to both user-and control plane packets. Selection of radio bearers of which packets are to be scheduled, management of necessary resources such as power, frequency, time, space, consideration of QoS associate with each radio bearers, channel state information for UEs, buffer status, interference situation, etc., and restriction or preference on a portion of the resource blocks (a set of resource blocks) due to interference coordination are all parts of DRA or PS responsibilities [OVE2014]. PS policy can be developed based on a number of performance metrics, e.g., maximum SINR, maximum fairness, or a trade-off of these two. The schedulers developed based on these metrics are respectively called as Max-min scheduler, Round Robin scheduler and Proportional Fair Scheduler and these are the conventional schedulers used in networks. In LTE, Max-min (MM) scheduler schedule an UE with the highest SINR in a resource block (RB) at any transmission time interval (TTI) and hence it provides the best average throughput performance, but at the least the fairness since UEs with good channel conditions are always scheduled and those UES with poor channel conditions such as the cell -edge UEs are scheduled very less, sometimes never at all. Round Robin (RR) schedules UEs with equal turn in a given period of time with equal amount of RBs, and hence it provides the best fairness, but usually at the worst average throughput since it does not consider the UE channel condition in account. The proportional Fair (PF) s cheduler provides a good trade-off of these two extreme performance schedulers by considering all UE’s past average throughput into account such that an UE who schedules very frequently in the past will be given less preference for scheduling even though t he channel condition is far better than other peer UEs. PF scheduler is usually more preferable to the other two schedulers because of it optimal average throughput and fairness performance, however its algorithm development is complex than RR and MM schedulers. There are many existing literatures can be found that examine the performance of these schedulers in HetNets from various perspectives in order to improve performance metrics such as throughput, fairness, and other QoS metrics. The role of PS in HetNets is very significant. PS is located in the eNB or macroBS. In HetNets, small cells such as femtocells, picocells, and relay stations are all connected to the eNB. The PS at eNB takes the decision of which UE at what time at which RB (a group of RBs) at what transmit power at which antenna port in which cell is to be scheduled based several parameters such as channel state information, precoding metrics, mobility, QoS requirements, UE location, UE serving cell, cell load, cell cooperation, interference mechanisms, etc. there is no specification from standardization bodies for PS principle and operation requirements, and network operators usually define PS strategy based on their respective network capacity, user demands and operator’s goals.

6.2.6 Inter-cell Interference Coordination Inter-cell Interference Coordination (ICIC) is a multi-cell RRM functionality that aims at controlling the interference by managing radio resources and exchanging information, e.g., resource usage status, traffic load state between cells. Both frequency-domain (FDD based) and time-domain (TDD based) ICIC are considered in LTE systems and the preferred method may be different in the uplink and the downlink [OVE2014]. Both FDD- and TDD-based ICIC are discussed elaborately in a chapter of this report.


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6.2.7 Inter-eNB CoMP Inter-eNB coordinated multi-point (CoMP) functionality coordinates multiple eNBs to improve the high data rate coverage, the cell-edge user throughput, and hence the system throughput [OVE2014]. The coordination is performed between eNBs by exchanging information via X2 interface in LTE systems. There are two types of CoMP in LTE systems: joint processing (JP) and coordinated scheduling/coordinated beamforming (CS/CB) CoMPs. In JP CoMP, all cells in the cooperating set transmit the same UE specific data to an UE. But in CS/CB, only the serving cell transmits the UE specific data to an UE after coordinating with all cells in the cooperating set. CoMP is detailed elaborately in a chapter of this progress report.

6.3 A Research Proposal: Multi-Objective Optimized Modular based Radio Resource Management for Evolved LTE-A Heterogeneous Networks Introduction In this research progress, we aim at proving a complete RRM solution to the evolved LTE HetNets – a non-trivial contribution that has never been contributed so far in the existing literature. The focus of the research is relatively broad er in the sense that RRM provides multiple functionalities, and addressing each functionality requires significant efforts. However, considering each RRM functionality individually as described before, around which most current researches on LTE-A RRM revolved, usually leaves with several other RRM functionalities affected because of simplistic assumptions or avoidance of those functionalities into consideration while solving the intended one. This is sort of ensuring local performance improvement, not a global one that requires several other RRM functions to be considered in the network wide RRM. Further, most researches have addressed a subset of RRM challenges such as higher spectral efficiency, enhanced interference mitigation, improved fairness, maximum resource utilization, etc. However, addressing a subset of challenges does not ensure mostly suitable RRM functionality since the proposed solution then suffers from other unaddressed performance issues. Furthermore, the current RRM functionalities are inter-dependent, i.e., changing one has impact on others because of their closed form of design. For example, if the requests from new radio bearers are not properly controlled and admitted by RAC function, the RBC function cannot be performed effectively because of degradation of QoS of on-going radio bearers, and hence results in poor bearer level maintenance function of RBC. In addition, the PS function will also be affected from not being able to schedule all the bearers as uniform as possible that may result in performance degradation of the network in terms of, for example, average user fairness QoS. Hence, to overcome this inter-dependency of RRM functionalities, we consider modular based development of RRM functionalities. Each RRM function will be developed independently from others, and maintain a local network states relevant only to its functionality and informs the joint optimizer about the network states and requirements. The optimizer then takes all RRM functionality module’s network states and requirements, and perform an optimization problem processing considering multi-objectives of the network as constraints; the outcomes of which is the decision rules on how each RRM module will execute operation in the next TTI (or every 5 TTIs) for example, such that s atisfactory performan ce of the LTE networks as specified by the network operator through multi-objectives as input. The optimizer also keeps a global record of each module as back-up in case there is a failure of any module. Hence, taking into account of this RRM function specific contribution by existing researches, we consider developing a complete solution of RRM functionalities on a unified platform, where the unification is provided by a joint optimizer. The optimizer provides decisions on RRM functional module centrally. All RRM modules then execute accordingly. This way, we can maintain both local (by each RRM module) and global (by the joint optimizer) network states and react promptly if there is any changes or malfunctions; allow modular based RRM developments for easy to update and to evolve without inter-dependency on other RRM functionalities; and provide complete RRM functionalities while considering multi-objective performance measures (a major challenges of current LTE networks) as constraints of the optimizer for satisfacto ry performance measures of all objectives LTE-A HetNet wide.


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RRM Functions The main Radio Resource Management (RRM) functions in a heterogeneous LTE/LTE-A cellular system include: 1. 2. 3. 4. 5. 6. 7.

Radio Bearer Control Radio Admission Control Connection Mobility Control Resource allocation or Packet scheduling Load balancing Inter-cell Interference Coordination (ICIC) and Inter-eNB CoMP

RRM Objectives The major challenges that current LTE networks face are from achieving the following multiple objectives that have significant impact on the overall performance of the LTE networks. So far now, most cases, maximum two to three objectives have been achieved by the existing literature while addressing any RRM functionality, leaving others unaddressed. 1. 2. 3. 4. 5. 6. 7. 8.

High spectral efficiency High capacity Enhanced interference mitigation/avoidance/alignment, Improved fairness, Reduced implementation and computational complexity, Maximum resource utilization, Improved QoS, and Low energy consumption

RRM Approaches Since LTE/LTE-A HetNets architecture is complicated, a single approach may not be sufficient to maintain satisfacto ry multiple objectives [YLL2014] achieved for all RRM functions. Hence, the solu tion may need multiple approaches to be considered and combined. For example, graph theory approach for interference mitigation, and cognitive radio approach for spectral efficiency improvement. Multi-objective specific approaches would be promising and effective for the overall performance satisfaction of all objectives for RRM functions. Followings are proved approaches or techniques for achieving objectives in Femtocell deployed LTE HetNets [YLL2014]. Many of these approaches are also applicable for relay nodes such as game theory. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Frequency Scheduling Cooperative Frequency Reuse Femtocell-Aware Spectrum Allocation Hybrid Spectrum Allocation Priority-based Spectrum Allocation Stochastic Resource Allocation Graph Theory Femtocell Clustering Cognitive Radio Game Theory Distributed Learning Power Minimization

RRM Modules Most RRM functionalities have cross impact, i.e., one functionality has considerable impact on others. For example, if proper radio admission control is not performed, the best performance of packet schedulers cannot be achieved. In this case even though the system level spectral efficiency is improved, but the fairness performance would be degraded. Hence, a modular based implementation of each RRM functionality may help overcome this inter-dependency issue.


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Each RRM function will be implemented as isolated module where the joint optimizer will decide optimal functionalities of all RRM module considering multiple objectives as constraints such that one RRM function has satisfactory impact on others. Each RRM functional module updates its current status without depending on others and informs the joint optimizer about its requirements. After taking all RRM modules network states inputs, the optimizer then performs optimization, the output of which is the decisions about how each module should act in the next TTI, e.g., packet scheduling among users, number of user requests to be accepted for admission control, keeping in multi -objectives constraints while processing joint optimization. The joint optimizer optim izes jointly such that each RRM function module have multi-objectives achieved satisfactorily. Since all RRM modules work satisfactorily, the overall system performance would become satisfactory as well. Further, modular implementation provides flexibility in updates on the existing RRM functionality. Furthermore, each module can inform the optimizer independently its current network status and requirements so that the optimizer’s joint decision will be global - which considers all RRM functions to operate satisfactorily without concerning other modules: a solution which is absent in todays current LTE networks, where RRM functionalities are implemented without any coordination centrally. Hence, modular based RRM functionality implementation gives freedom to evolve independently considering the underlying wireless conditions relevant to that module’s functionality only and at the same time ensures satisfacto ry network performance by helping joint optimizer that processes optimization decisions consid ering multi-objectives constraints. Since the next generation mobile broadband, i.e., 5G will also be deployed as HetNets but more densely, and almost all of these RRM functionalities will also be a part of 5G networks, adopting the modular based RRM fun ctionality implementation based on multi-objectives in 5G networks will play a significant role on the network radio resource utilization and the user quality of experience (QoE).

6.4 Summary In this chapter, we have discussed on major RRM functionalities that are present in traditional cellular networks in addition to advanced intelligent networks such LTE-Advanced and beyond systems. Since RRM has direct impact on the overall system capacity, we have identified and discussed on most of the basic functionalities and a number of advanced RRM functionalities. Most basic RRM functionalities such as RAB, RAC, and CMC have been described in detail in this chapter, while advanced RRM functionalities such as enhanced ICIC and CoMP have already described in detail in the chapter 4. Because of the consideration of the HetNet feature on the current network evolution and also proposed to be the same for the future 5G cellular networks that will be more dense, we have contextualized these RRM functionalities for HetNets scenarios and have pointed out a number ways to adapt the existing RRM functionalities in HetNet scenario. In the view of that, finally, we have proposed a multi-objective and modular based complete RRM functionality implementation for future 5G networks and described the concept of implementation in detail.


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CHAPTER 7 CONCLUSION 7.1 Discussion In this progress report, we discuss on the literature review that we have been carried out in the semester – August 2014. The main intention for the literature review is to gain knowledge on the existing literature in the area of research interest . In line with that, we have carried out an extensive review on the existing cellular mobile networks to figure out potential research problem that can be addressed in the dissertation. Since the demand of mobile services growing rapidly with an ever increasing d emand for high data rate to get access to rich IP-based multimedia services using mobile networks, the current mobile networks cannot become able to address the high data rate demand from the continual increasing number of mobile phone users. This necessit ates to think through the next generation of cellular mobile networks, i.e., 5G mobile networks. 5G is still in the very early-stage of research, and hence there is no clear specifications on its network’s features, services, and capabilities that the 5G will address. However, as the research on 5G networks has already been started, and there are a number of researches that have addressed the question: what will the 5G networks be in terms of major aspects such as network requirements, enabling technologies, network architectures, and network capacity. In chapter 2 of this report, we have discussed the 5G networks in terms of these aspects. There are mainly two directions that the 5G network architecture would be evolved - either through centralized or distributed networking paradigms. In centralized networking, the control plane and the data plane functionalities are separated from all networking elements that involve in data forwarding tasks from one point to another, and all the control functionalities of these elements are made logically centralized to a single entity. The entity that governs the centralized control functionalities of all the data path elements is called controller. The controller is responsible for commanding the data path elements on how a user-specific information should be forwarded in the network. In literature, this approach of networking is called software defined networking, and is one of the enabling candidates for the 5G networking. There have already been a number of research proposals in the existing literature that have addressed how to apply the software defined networking concept to wireless environments, specifically to, LTE, WiMAX, and WiFi standards. In chapter 5 of this report, a number of such research proposals on software defined networking application to LTE, WiMAX and WiFi have been explained. The other networking paradigm for the 5G is the traditional distributed networking where the control of the network is distributed over the network entities, and the information is forwarded from one point to another based the predefined strategies implemented in the entities. The traditional homogeneous cellular networks are based on the distributed networking principle where macroBSs, distributed over the network, control the network’s wireless interface functionalities. Based on the same distributed networking but different from other generations (e.g., up to 3G), the fourth generation (4G) mobile systems, also termed as LTE-Advanced systems, have considered small cell deployment, to enhance the network capacity, in the coverage of the traditional macrocell which results the 4G networks deployed in heterogeneous form. The small cells for HetNets have been discussed in chapter 3. However, even with HetNets, the current LTE networks have been facing difficulty to meet the overall network capacity in order to address the high data rate demand from the users and the continual increase in mobile user subscriptions. In order to meet the ever increasing network capacity demand, standardization bodies such 3GPP have already proposed a number of new techniques based on cooperative communications such as eICIC, CoMP, and Networked MIMO in different releases of LTE as evolutionary steps. We have discussed in detail the cooperative communications in HetNets © RONY KUMER SAHA AND CHAODIT ASWAKUL

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in chapter 4. However, even with the introduction of all these new techniques, it is predicted that this incremental improvement in network capacity will not be sufficient to meet the exponentially growing number of users and high data rate demand in near future. Hence, to achieve the high network capacity demand for future 5G networks, the major technology dimensions that have proposed by most industry experts and academicians in the current distributed networking platform are dense deployment of small cells (DenseNets), large spectrum aggregation, and high spectral efficiency. For large spectrum aggregation, since the micro wave frequencies, mostly below 3GHz, have already been occupied, in order to increase the spectrum bandwidth and because of high spectrum availability (in GHz range), mmWave frequen cy bands have been proposed to include into the 5G networks. With its stringent characteristics, e.g., low coverage (about 200 meters) and LOS propagation, the use of mmWave spectrums has been proposed for small cell coverage and backhau l networks. To achieve high spectral efficiency, the massive MIMO technology where magnitudes of 100 antennas will be installed to increase the number of parallel paths in order to achieve high spatial multiplexing gain to enhance the received signal strength has been proposed to include into the 5G networks. Further, cooperative communications where network nodes will cooperate one another to reduce the interference level, to increase signal diversity, and to improve resource sharing have been considered. In chapter 4, we have detailed these features of cooperative communications. The third dimension that has been considered the most impactful and most viable is the dense deployment of small cells in the coverage of the macrocell. In dense small cell deployment, small cells may operate on the same frequency as that of the macrocell or at different frequencies such as at mmWave spectrum and can overlap one another. The interference in such a dense deployment scenario will be managed with the proper coordination of small cells and the macrocell by exchanging information via backhaul networks. Since RRM has tremendous impact on the network capacity performance, in chapter 6 we have discussed major RRM functionalities that have been standardized by 3GPP for beyond LTE -Advanced (4G) networks and will eventually also be expected to be present in 5G networks. We have proposed a complete RRM implementation for 5G networks that will be multi-objective concerned for optimal performance and modular based for flexibility in operat ion, management, and maintenance.

7.2 Major Findings Having gained knowledge so far through the literature review, few major findings on 5G networks are listed out in the followings: 

5G networks is in the very early stage of research, and an extensive level research work is in immediate need.



Though 5G is still unspecified, most feasibly the 5G network architecture will follow the current distributed paradigm with an evolution of current LTE networks where other radio access networks such as WiFi, WiMAX will co-exist with flexible cooperation with each other. This is because of the fact that a complete or even partly change in the network architecture will result in huge investment and need associated changes in the network device level. Further, networking communities of one domain do not easily intend to leave the control to another.



The 5G network architecture can be evolved through mainly three dimensions, namely network node, network control and network performance that we have proposed in chapter 2 for 5G networks. The 5G network architecture would be (1) from a single-user-served based node such as UE to a maximum -user-served based node such as macroBS centric; (2) from fully centralize (e.g., WSDN) to fully distribute controlled (e.g., LTE


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evolution); (3) and from extremely low (e.g., network latency) to extremely high performance (network capacity) based. 

Though several drivers of 5G networks have been identified in the literature so far, the dominant of all is the network capacity enhancement. Because, the network capacity availability is the most basic need from the user side demands whom would otherwise not possible be accommodated and served with required QoS.



As we have identified by using Shannon’s capacity formula to HetNets, the main enabling technologies for the 5G network capacity are more system bandwidth through mmWave spectrum aggregation to the exiting microwave spectrum, high spectral efficiency through massive MIMO by achieving more parallel links (i.e., high multiplexing gain) between network nodes (e.g., BSs and UEs) and through cooperative communication among network nodes by reducing interference and improving received signal strength, and network densificatio n through densely deployed small cells (DenseNets) in the coverage of macrocell by reusing the same spectrum as many times as possible in small cell tiers.



In DenseNets, the interference is the main bottleneck as small cells such as femtocells are expected to be deployed unplanned by the users and operators may not have accurate knowledge of the network topology. In addition, when small cells are deployed densely overlapping coverage areas of one another and are operated on the same frequency as the macrocell, the interference becomes significant from both intra-tier (i.e., between small cells) and inter-tier (e.g., macrocell and femtocell) interference. In addition, users in the macrocell-edge areas are suffered from weak received signal strength and strong inter-cell interference. In such as DenseNets which is susceptible to strong interferences from several scenarios, network cooperation between nodes is a must that will be employed in the 5G networks. Many network cooperation techniques have already been standardized in 3GPP LTE such CoMP, Networked MIMO, eICIC and can also be a strong candidates for addressing the interferen ce issue in 5G DenseNets.



Appropriate backhaul networks availability is considered as on the major enabler for proper cooperation among network nodes which help carry information regarding user data, control signaling from one node to another. If appropriate backhauling cannot be guaranteed, the network will face poor performance from cooperation in terms of latency, data rate, and other QoS. Hence, care must be taken for backhaul network deployments based on the cell specific scenarios such as coverage, data rate demand, average volume of data generation, user mobility pattern, etc. we have described various aspects of backhaul networks in chapter 4.



With an introduction of cooperative communication, a number of issues will arise in different layers of the protocol stacks of non-cooperative networks and hence, may need relevant modification in layer-wise basis to address the issues which we have introduced in chapter 4.

7.3 Future Research Directions After having been through the literature review and identified the major findings, particularly on the 5G network capacity realization, we have set primarily to carry out the research for the dissertation on the following topic part -wise: capacity analysis of 5G DenseNets with mmWave and massive MIMO as follows: Part I: Single macrocell scenario In this part, we will analyze the impact of density of small cells on capacity overlaid in the coverage of a single macrocell scenario . The followings cases are considered primarily to be exploited in single macrocell scenario: 1.

We exploit the percentage of small cells such as picocell, femtocell, relay, and RRH in the scale of 100, i.e., we will vary the number of each type of small cells, given a total number of small cell, and analyze the impact on the total capacity.


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2.

We will exploit the same frequency deployment and the different frequency deployment of the small cells from the macrocell.

3.

We will also exploit two cases of cooperation: one with cooperation of small cells among themselves as well as with the macrocell. The other case is non-cooperative, i.e., no cooperation among cells will be considered.

4.

Under cooperation case, we will exploit backhaul requirements in two cases: one ideal backhaul (high capacity, low latency) such as optical fiber and the other with non-ideal backhaul (limited capacity, high latency) such as DSL cable, copper cable, or NLOS microwave wireless.

Part II: Multi-cell scenario We then extend the single macrocell scenario case in part I to multi-cell scenario case. The followings are expected to be exploited in this part: 1.

We will exploit the network cooperation among multiple macrocells also called Networked MIMO or CoMP and compare it with no-cooperation among macrocells in terms of network capacity. Under this scenario, we will exploit the following cases:   

2.

JP CoMP CS/CB CoMP DCS CoMP

We will then exploit the impact of the size of cooperating set, i.e. cluster size for network cooperation on network capacity and backhaul overhead performances under each of these CoMP case.

Part III: Introduction of mmWave spectrum In this part, we will extend the part II by introducing mmWave spectrum to small cells and then compare the network capacity with part II as base line. Part IV: Introduction of massive MIMO In part IV, we will extend the part III by introducing massive MIMO to macrocells as well as small cells and then compare the network capacity with part III as base line. In each part, we will verify the concepts analytically and will simulate the relevant scenarios to analyze the capacity performance of 5G networks.


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[HWE2014] H. Wen, “Virtualization and Software-Defined Infrastructure Framework for Wireless Access Networks”, Master’s Thesis, ECE Dept., McGill University, Montreal, Canada, April 2014. [NES2013] 3GPP, “Network Sharing: Architecture and Functional Description”, 3GPP Technical Specification 23.251, Version 11.4.0 Release 11, Jan. 2013. Chapter 6 [OVE2014] 3GPP, 3GPP TS 36.300 V12.3.0 (2014-09). 3rd Generation Partnership Project; Technical Specificatio n Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description; Stage 2 (Release 12). [PCCA2014] 3GPP, 3GPP TS 23.203 V13.1.0 (2014-09). 3rd Generation Partnership Project; Technical Specificatio n Group Services and System Aspects; Policy and charging control architecture (Release 13). [GPRS2014] 3GPP, 3GPP TS 23.401 V13.0.0 (2014-09). 3rd Generation Partnership Project; Technical Specificatio n Group Services and System Aspects; General Packet Radio Service (GPRS) enhancements for Evolved Universal Terrestrial Radio Access Network (E-UTRAN) access (Release 13). [RED2013] HSPA, LTE, EPC Knowledge Nuggets, 2013, Red banana wireless Ltd. [CCO2012] C. Cox, “An Introduction to LTE, LTE-Advanced, SAE and 4G Mobile Communications”, John Wiley & Sons Ltd, 2012, United Kingdom . [ALO2010] A. Lobinger, “Load Balancing in Downlink LTE Self-Optimizing Networks”, IEEE, 2010. [ISD2012] I. Siomina and D. Yuan, “Load Balancing in Heterogeneous LTE: Range Optimization via Cell Offset and Load-Coupling Characterization”, IEEE ICC 2012 - Communication QoS, Reliability and Modeling Symposium. [SLA2011] S. Landstorm et al, “Heterogeneous Networks-Increasing Cellular Capacity”, Ericsson Review, 2011. [JWK2014] J. Wannstro and K. Mallinson, “Heterogeneous Networks in LTE”, 3GPP Mobile Brodband Standard. Available: http://www.3gpp.org/technologies/keywords-acronyms/1576-hetnet.Download: Dec. 2014. [WAK2012] W. A. Mengistie and K. Ronoh, “Load Balancing in Heterogeneous LTE Networks”, Thesis, Dept. of Scien ce & Technology, Linkoping University, Sweden, 2012. [DNE2005] D. Niyato and E. Hossain, “Call Admission Control for QoS Provisioning in 4G Wireless Networks: Issues and Approaches”, IEEE Network, September/October 2005, pp.5-11. [EZT2008] E. Z. Tragos et al, “Admission Control for QoS Support in Heterogeneous 4G Wireless Networks”, IEEE Network, May/June 2008, pp.30-37. [ZLU2012] Z. Luan et al, “Using Game Theory for Radio Resource Management of RRC Layer in LTE-A”, ICACT2012, February, 2012, pp.41-44. [YLL2014] Y. L. Lee et al, “Recent Advances in Radio Resource Management for Heterogeneous LTE/LTE-A Networks”, IEEE Communications Surveys & Tutorials, vol.16 (4), 2014, pp. 2142-2180.

APPENDIX 1 © RONY KUMER SAHA AND CHAODIT ASWAKUL

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LIST OF ABBREVIATIONS Abbreviated Form

Full Form

3G 3GPP 4G 5G ACCS ACK aGW API APN APN-AMBR ARP BBU BDP BS BW C/I CA CC CMC CoMP C-plane CQI C-RAN CRE CRS CS/CB CSG CSI D2D DAS DeNB DenseNet/s DOFDM DPS DRA DSL eICIC eNB EPC EPS E-RAB FDD FTTC GBR GPS GW HARQ HeNB HetNet/s HII HSS ICIC ID IMT-advanced

Third Generation Third Generation Partnership Project Fourth Generation Fifth Generation Autonomous CC Selection Acknowledgement Aggregated Gateway Application Programmable Interface Access Point Name APN Aggregate Maximum Bit Rate Priority of Allocation and Retention Baseband Processing Unit Bandwidth-Delay Product Base Station Bandwidth Carrier-to-Interferen ces Carrier Aggregation Component Carrier Connection Mobility Control Coordinated Multi-Point Communication Control-Plane Channel Quality Indicator Cloud Infrastructures Radio Access Network Cell Range Expansion Cell Specific Reference Signal Coordinated Scheduling/Coordinated Beamforming Closed Subscriber Group Channel State Information Device-to-Device Distributed Antenna System Donor Evolved NodeB Dense Heterogeneous Network/S Discontinuous Orthogonal Frequency Division Multiplexing Dynamic Point Selection Dynamic Resource Allocation Digital Subscriber Line Enhanced Inter-Cell Interference Coordination Evolved NodeB Evolved Packet Core Evolved Packet System Evolved-UTRAN Radio Access Bearer Frequency Division Duplex Fiber to the Cell Guaranteed Bit Rate Global Positioning System Gateway Hybrid Automatic Repeat Request Home eNB Heterogeneous Network/S High Interference Indicator Home Subscriber Server Inter-Cell Interference Coordination Identification International Mobile Telecommunications-Advanced


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IoT IP ITU-R JT/JR LAN LB LOS LTE LTE-Advanced M2M MIMO MM MME MMSE mmWave MU MU-MIMO NACK NLOS OFDM OI PBCH P-Cell PCI PDB PDCCH PDSCH, PELR PF PGN P-GW PMI PMP PON PS PSS QCI QoE QoS RA RAC RAN RAT RB RBC RI RNTP RR RRC RRH RRM RS RSRP RSRQ S-Cell SCTP SDF SDN SGN SIB

Internet of Things Internet Protocol International Telecommunication Union Radio Standards Sector Joint Transmission/Joint Reception Local Area Network Load Balancing Line-of-Sight Long Term Evolution Long Term Evolution-Advanced Machine-to-Machine Multiple-Input Multiple-Output Max-Min Mobility Management Entity Minimum Mean-Square-Error Millimeter Wave Multi-User Multi User-MIMO Non-Acknowledgement Non/Near LOS Orthogonal Frequency Division Multiplexing Overload Indicator Physical Broadcast Channels Primary Cell Physical Cell ID Packet Delay Budget Physical Downlink Control Channel Physical Downlink Shared Channel Packet Error Loss Rate Proportional Fair PDN Gateway Node Packet Data Network Gateway Precoding Matrix Indicator Point-to-Multipoint Passive Optical Network Packet Scheduling Primary Synchronization Signal QoS Class Identifier Quality of Experience Quality-of-Service Random Access Radio Admission Control Radio Access Network Radio Access Technology Resource Block Radio Bearer Control Rank Indicator Relative Narrowband Transmit Power Round Robin Radio Resource Control Remote Radio Head Radio Resource Management Relay Station Reference Signal Reference Power Reference Signal Received Quality Secondary Cell Stream Control Transmission Protocol Service Data Flow Software Defined Network Serving Gateway Node System Information Block


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SINR SON SRS SSDL SSS SSUL SU-MIMO TCP TDD TTI UDP UE UE-AMBR UMTS U-plane US-RS VNI WiFi WiMAX WSDN WWRF

Signal-to-Interference Ratio Self-Optimizing Network Sounding Reference Signal Strongest Received Signals for Downlink Secondary Synchronization Signal Strongest Received Signals for Uplink Single User-MIMO Transmission Control Protocol Time Division Duplex Transmission Time Interval User Datagram Protocol User Equipment UE Aggregate Maximum Bit Rate Universal Mobile Telecommunications System User-Plane UE Specific Demodulation Reference Signal Visual Network Index Wireless Fidelity Worldwide Interoperability for Microwave Access Wireless Software Defined Network Wireless World Research Forum


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