Multicasting in LTE-A Networks Enhanced by ... - Semantic Scholar

Multicasting in LTE-A Networks Enhanced by Device-to-Device Communications Massimo Condoluci, Leonardo Militano, Giuseppe Araniti, Antonella Molinaro, Antonio Iera University Mediterranea of Reggio Calabria, Italy, DIIES Dep. e-mail: [massimo.condoluci|leonardo.militano|araniti|antonella.molinaro|antonio.iera]@unirc.it Abstract—The growing demand for group-oriented services has recently attracted the interest of the research community. Several proposals have been designed for the most promising broadband wireless system, Long Term Evolution-Advanced (LTE-A), to enhance key performance figures such as spectrum efficiency, data rate and user satisfaction. Starting from the standard proposals for multicasting and broadcasting systems, in this paper we investigate the potentialities of Device to Device (D2D) communications for enhancing the performance of multicast communications. While keeping the objective of serving all users in a multicast group, as for the Conventional Multicast Scheme (CMS), more performing Modulation and Coding Schemes (MCS) are adopted in the path from the base station to users, by leveraging D2D links to serve nodes with worse channel conditions. Radio resources for the activated transmission links from the base station and for the D2D links are managed in order to maximize the aggregate data rate. A simulative performance evaluation in a wide set of scenarios shows the significant achievable improvements. Keywords—LTE-Advanced, Multicast, Device-to-Device, Radio Resource Management

I.

Introduction

T

HE growing demand for group-oriented services (i.e., multicast and broadcast) has led to the definition of new standards and multimedia applications for mobile terminals. Long Term Evolution-Advanced (LTE-A) [1] seems to be the most promising broadband wireless system able to support such services with several benefits to the user and the network sides. For instance, it guarantees high data rates in downlink and uplink directions, efficient Quality of Service (QoS) management, high spectrum efficiency and high system capacity. While these features make LTE-A very attractive for grouporiented services, researchers are active in studying solutions to effectively handle the diversity of channel quality experienced by users in the same Multicast Group (MG) and to efficiently use the available resources [2]. The baseline proposal for multicasting and broadcasting systems in LTE [3] is also called Conventional Multicast Scheme (CMS) in the literature, where all User Equipments (UEs) are served in every Transmission Time Interval (TTI), but the total data rate is limited by the user with the worst channel conditions. As a consequence, a poor performance level is achieved in terms of network data rate and satisfaction The research of Massimo Condoluci is supported by European Union, European Social Fund and Calabria Regional Government. This paper reflects the views only of the authors, and the EU, and the Calabria Regional Government cannot be held responsible for any use which may be made of the information contained therein.

experienced by the users with good channel conditions. Alternative approaches exist, such as for example the Opportunistic Multicast Scheme (OMS) [4]. In line with OMS, not all UEs are served in a given time interval and the system data rate is optimized according to the channel quality. A further investigated approach is based on the multicast subgrouping policies [5] [6], [7], whereby the multicast destinations are grouped into different subgroups depending on the UE channel quality.

In this paper, a scheme is proposed which exploits another flourishing research field within LTE-A systems, namely Device-to-Device (D2D) communication. In particular, UEs being close to each other can activate direct links by using cellular communication resources [8].There are a number of scenarios where D2D communications can provide significant improvements beyond classic relaying approaches in LTEcontext. For example, data offloading for proximity based applications is investigated in [9]. Whenever communication is inherently local in scope, D2D links could be substantially more efficient than conventional traffic forwarding through a base station [10] and [11]. Furthermore, D2D communications could also enable mobile terminals to act as relays, thus extending the network coverage or supporting content sharing among users [12]. These observations led us to the idea of bringing D2D communications into the multicast services framework, and investigating the potentialities of adopting D2D communications to improve the mentioned CMS solution. In particular, the proposed scheme keeps the philosophy of the standard CMS to serve all UEs in every TTI, but it takes into consideration the possibility that not all UEs are to be served directly by the base station and D2D communications can be leveraged to reach all users in the group. This allows the base station to use better performing Modulation and Coding Schemes (MCS), while nodes with worse channel conditions are served through D2D links. A simulative performance evaluation in a wide set of scenarios will show significant data rate improvements when comparing the proposed solution with the CMS solution, given a fixed amount of globally allocated resources.

The remainder of the paper is organized as follows. In the next section, the reference system model and service scenario are introduced. Section III introduces the proposed algorithm in details, while performance evaluation results are given in section IV. The reader will find some conclusive remarks in the last section.

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II.

Reference System and Service Scenario

Focus of this paper is on a multicast single-cell scenario in LTE-A networks. The LTE-A downlink air interface is based on the Orthogonal Frequency Division Multiple Access (OFDMA). Spectrum is managed in terms of Resource Blocks (RBs) and in the frequency domain each RB corresponds to 12 consecutive and equally spaced sub-carries. One RB is the smallest frequency resource, which can be assigned to a UE. The overall number of available RBs depends on the system bandwidth configuration and can vary between 6 (1.4 MHz channel bandwidth) and 100 (20 MHz). Through a carrier aggregation scheme, up to five Component Carriers (CCs) can be aggregated in order to reach a 100 MHz channel bandwidth. A Packet Scheduler (PS) is implemented at the Medium Access Control (MAC) layer [13]. The main functionality of the PS is to efficiently handle the resource allocation in the time and frequency domains. The Frequency Domain Packet Scheduler (FDPS) is in charge of the spectrum management, by assigning the adequate number of RBs to each scheduled user and by selecting the MCS for each RB. These procedures are conducted based on the Channel Quality Indicator (CQI) feedback transmitted by the UE to the base station. The CQI is associated to the maximum supported MCS [1]; Table I shows the CQI values defined by the LTE-A standard. Transmission parameters (i.e., MCS) are adapted at every CQI Feedback Cycle (CFC), which can last one or several Transmission Time Interval (TTI, equal to 1 ms) [1]. Also for D2D communications to be activated in the cell, the base station is in charge of allocating the resources and determining the associated MCSs. TABLE I. CQI index 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Modulation Scheme QPSK QPSK QPSK QPSK QPSK QPSK 16-QAM 16-QAM 16-QAM 64-QAM 64-QAM 64-QAM 64-QAM 64-QAM 64-QAM

CQI-MCS Mapping [14] Code rate x 1024 78 120 193 308 449 602 378 490 616 466 567 677 772 873 948

Efficiency [bit/s/Hz] 0.1523 0.2344 0.3770 0.6016 0.8770 1.1758 1.4766 1.9141 2.4063 2.7305 3.3223 3.9023 4.5234 5.1152 5.5547

Minimum Rate [kbps] 25.59 39.38 63.34 101.07 147.34 197.53 248.07 321.57 404.26 458.72 558.72 655.59 759.93 859.35 933.19

Fig. 1.

D2D-supported Conventional Multicast Scheme.

Multicast Scheme (D2 -CMS) where all UEs are served in every TTI by considering also the possibility to activate D2D links to improve the system performance. In fact, users that are in coverage for a D2D communication can activate such a direct link according to the instructions received by the eNodeB. In these cases one of the UEs will act as a relay node receiving the content from eNodeB and then forwarding it to the connected device. In the reference scenario, as depicted in Fig. 1, D2D communications can be either unicast or multicast; thus, a relay node can serve one or more connected UEs. Concerning the resource allocation, we assume that downlink resources are allocated for communications from eNodeB to the relay nodes, while uplink resources are allocated for D2D communications. This choice is motivated by the fact that reusing downlink resources is more challenging than reusing uplink resources in the worst case of a fully loaded cellular network, as demonstrated in [15]. Moreover, the use of uplink resources for D2D links gives the possibility of freeing resources in the downlink that could be used for other services within the cell. We assume that either Multiple Description Coding or Scalable Video Coding techniques are used for the real time service delivery, so that also low data rates will guarantee some quality of service. As a term of comparison, we also consider the Opportunistic Multicast Solution (OMS), whereby only the subset of nodes maximizing the data rate is served in every TTI. For a fair comparison, the same amount of radio resources is dedicated to the multicast service by the three solutions, either in downlink only (CMS and OMS) or in downlink and uplink (D2 -CMS). III.

The Proposed D2 -CMS Solution

A. Assumptions and Spectrum Efficiency Matrix computation The reference service scenario for this paper is a multicast real-time service, with a group of UEs interested in the same content served by an LTE-A cell, for instance students oncampus who are accessing a content of common interest. To handle the multicast service, the eNodeB collects the CQI feedbacks from each UE belonging to the group, determines the corresponding CQI level, and then sends the data to all or a subset of UEs. Conventional Multicast Scheme (CMS) is a solution whereby all UEs are served in every TTI. The node with the lowest CQI level limits the data rate for all nodes, as all available resources (RBs) are all allocated to the single activated CQI level. In this paper we propose a D2D-supported Conventional

Let N be the number of UEs in the multicast group. Let N dl j ⊆ N be the set of UEs that can be served by activating the MCS corresponding to CQI level j ∈ J in downlink. If j is the CQI level considered for the downlink, then the total attainable data rate on this link depends on the cardinality of dl N dl j , on the number of assigned resources R (the number of RBs in the frequency domain), and on the achieved data rate per assigned RB. This latter parameter, named bdlj , depends on the CQI level j, as reported in the minimum data rate field in Table I. The number of assigned RBs Rdl can vary from 1 to R, where R is the total number of resources dedicated to the multicast service. For a fair comparison of the solutions, in the remaining part of the paper R is considered as a fixed value, while the percentage of R RBs used in downlink (i.e., Rdl ) and

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uplink (i.e., Rul ) can vary. Thus, in general, the total data rate obtained by activating the MCS corresponding to CQI level j in downlink is given by: dl dl Ddlj = |N dl j | · R · bj .

(1)

Being N dl j in general only a subset of N, the remaining set of nodes N rj = N − N dl j is potentially served through D2D links. When D2D links are activated, some or all elements in r N dl j can act as relay nodes toward the elements in N j . Let dl us define RN j ⊆ N j as the subset of nodes that receive the data when considering CQI level j in downlink and act as relay nodes. The eNodeB will determine the set of nodes N d2d ⊆ N rj j that can be served through D2D links by the nodes in RN j . In particular, we define DN mj ⊆ N d2d as the set of nodes (one or j more) served by relay node m ∈ RN j through D2D links, RBmj as the RBs allocated to relay node m, and bd2d j,m as the data rate achieved with 1 RB assigned to a D2D link activated between the relay node m and the served nodes in DN mj . This latter value strictly depends on the CQI level on the D2D link. Since the objective of the proposed solution is to serve all UEs in the system, in case of multicast communications from a relay node, the activated CQI level is the one associated to the worst channel conditions among the served nodes on D2D links. To the aim of determining which of the N rj nodes can be reached with D2D links from the relay nodes, the spectrum efficiency of a D2D link is considered, as defined in [16]. Specifically, let Rmn (bit/s) be the highest achievable data rate on a D2D link connecting the m-th and n-th devices with a bandwidth of BWmn (Hz). Then, the spectrum efficiency emn of this D2D link can be defined as emn = Rmn /BWmn (bit/s/Hz), which is theoretically equivalent to the capacity of the D2D link between devices m and n for unit bandwidth. For each activated CQI level j, a Spectrum Efficiency Matrix (SEM) can be defined that includes the emn values for all the links between the N dl j nodes served in downlink (the matrix rows) and the remaining N rj nodes (the matrix columns). A SEM example is reported in Table II. A zero in the mn cell of the SEM indicates that a D2D link cannot be activated between r node m ∈ N dl j and node n ∈ N j . A node n ∈ N rj will be associated to the relay node m ∈ N dl j that has the highest spectral efficiency emn in the SEM. In fact, especially with dense node distribution in the cell, it might happen that more than one node can act as a relay. If multiple potential relays have the same efficiency values, the eNodeB chooses the one that minimizes the total number of relay nodes in the cell. Indeed, activating more relay nodes means that the available resources (the fixed amount of RBs R) are to be shared among more direct links, with a negative impact on the overall system data rate. Therefore, a node n ∈ N rj is associated to the relay node m ∈ N dl j with the highest spectral efficiency in the SEM and serving the highest number of nodes over D2D links. Once the eNodeB knows how many D2D links are going to be activated in a given TTI, it can define how the total available RBs R are shared between the downlink and uplink D2D links. In general, a D2D link is expected to need a fewer resources compared to those needed for an eNodeB-to-relay communication, thanks to shorter distances and better channel

TABLE II.

hhhh h

Spectrum Efficiency Matrix.

Other nodes hhh hhhh node 1 node 2

DL-served node node 4 node 5 ... node m

e41 e51 ... em1

e42 e52 ... em2

...

node n

... ... ... ...

e4n e5n ... emn

quality conditions. However, this is not necessarily true and it actually depends on the node distribution in the cell and the downlink choices of eNodeB. B. The D2 -CMS Algorithm Step by Step The D2 -CMS scheme is described in details in Algorithm 1. The main idea is to test all CQI levels to be activated in downlink (maximum 15 for the LTE-A system). A given CQI level j can be activated only if all nodes in the cell can be served, either with downlink or uplink (D2D) resources. Among the candidate solutions meeting this requirement, the one maximizing the overall data rate will be chosen. Noteworthy, in the worst case when none of the tested CQI levels meets the requirement of serving all UEs by activating also D2D links, the classic CMS solution will be adopted (case j = 1 in the Algorithm). Going into the details, the first eleven lines in Algorithm 1 list how the information is collected by the eNodeB about the UEs served in downlink N dl j , the UEs serving as relay nodes RN j , and the nodes not being served in downlink but associated to the selected relay nodes DN mj . In particular, the required configuration for the implementation of the conservative policy on the D2D links is reported in lines 7-9. For each tested CQI level j ∈ J in downlink, the eNodeB verifies whether all users can be served (possibly by activating also D2D links) and whether there are enough RBs to assign at least one RB in downlink and the corresponding RBs needed to relay the data on the D2D links (line 14). In particular, the correspondence of one RB in downlink and the RBs allocated to a relay node is determined by the dbdlj /bd2d j,m e fraction. In fact, the data per RB received by a relay node depends on bdlj , and the number of RBs needed to relay this amount of data over the D2D link is obtained by rounding up the bdlj /bd2d j,m fraction. If the two cited conditions cannot be met, then the solution is discarded by assigning zero to the aggregate data rate (line 29). For the other cases when the conditions can be met, the eNodeB needs to determine how the total RBs R are distributed between the downlink and the D2D uplinks. The available resources are allocated by following a Round Robin policy, where, initially, one RB is allocated in downlink, and then, for each of the D2D links, the needed RBs are allocated such that all received data can be relayed. In particular, the eNodeB checks if each relay node m ∈ RN j needs additional RBs to relay the data received in downlink and if these RBs are available before allocating them, see lines 19-231 . 1 If the RBs are not enough to equally serve all D2D links, the relay nodes to serve first are selected based on the capability to convert an assigned RB into a data rate; this depends on the number of UEs associated to relay node m and the data rate per RB obtained with the CQI activated on the D2D link (determined by bd2d j,m ).

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When all RBs are allocated, the total data rate D obtained with the tested solution is computed. The total data rate is given by the sum of the data rate in downlink and the data rates over the activated D2D links in a given TTI, see line 27. Finally, for both the proposed schemes, the eNodeB will select the CQI level to be activated in downlink and the corresponding data rate D that maximizes the system data rate, see line 31. Algorithm 1: The D2 -CMS implementation.

1 2 3 4 5 6 7 8 9 10 11 12 13

Data: J the set of potential CQI levels to be activated, R available RBs Result: Total data rate D; MCS level j to activate in downlink; MCS levels for D2D links for j := min(J) → max(J) do bdlj ← data rate per RB in downlink with CQI level j dl r N dl j ; N j = N − N j ← UEs served/not served in downlink r for all n ∈ N j do dl Select m ∈ N dl j as relay node if emn is the highest ∀m ∈ N j and serves the highest number of UEs Add node m to set RN j and node n to set DN mj if emn is lowest ∀n ∈ DN mj then Set bd2d j,m according to CQI level between m and n end end end . Start resource allocation if CQI level j ∈ J meets constraints on serving all UEs P in N mwith at least 1 RB P if ((N dl |DN j |) == |N|) ∧ (R ≥ 1 + dbdlj /bd2d j + j,m e then m∈RN j

15 16 17 18 19

r=

20

22 23

end

25

end dl dl D( j) = |N dl j | · R bj +

26 27

30 31

bd2d j,m

end

24

29

m d2d Rdl bdl j −RB j b j,m

if (R − (Rdl + Rul + r) ≥ 0) then RBmj = RBmj + r; Rul = Rul + r end

21

28

m∈RN j

Rdl = Rul = 0; RBmj = 0, ∀m ∈ RN j while (Rdl + Rul < R) do Rdl = Rdl + 1 for all m ∈ RN j do . Check if more RBs are needed for m to relay the data and allocate if available if ((Rdl bdlj > RBmj bd2d j,m ) then

14

P m∈RN j

m dl dl min[(RBmj bd2d j,m ), (R b j )] · |DN j |

else

D( j) = 0 . CQI level not of interest end Activate configuration j ∈ J for which system data rate D is maximum

IV.

Performance Evaluation

A thorough simulative analysis has been conducted in Matlab where a distribution of UEs over a concentrated area (100x100 m) at the cell-edge has been considered to reflect a typical on-campus scenario. Two different scenarios have been considered: •

Scenario A: the multicast group size N is set to 200 while a variable number of RBs R is considered in the range [10 − 100] RBs;

•

Scenario B: the number of available RBs R is set to 100 RBs and the number of UEs N is in the range [20 − 200].

Besides these two scenarios an analysis is performed for a wide set of UEs distributions within the cell in sample configurations of number of RBs R and number of UEs N. The network is assumed to adopt Time Division Duplexing (TDD). Channel conditions for each UE are evaluated in terms of Signal to Interference and Noise Ratio (SINR) when path-loss, shadowing, and multipath fading affect the signal reception [17]. The effective SINR, calculated through the Exponential Effective SIR Mapping (EESM), is eventually mapped onto the CQI level ensuring a block error rate (BLER) smaller than 10% [17]. Main simulation parameters and channel information are listed in Table III. The maximum range for a D2D link connection is set to 50 m [18]. Outputs are achieved by averaging a sufficient number of simulation results to obtain 95% confidence intervals. TABLE III. Parameter Cell radius Frame Structure UL/DL configuration Carrier Frequency eNodeB Tx power D2D node Tx power Antenna gains and patterns (Tx and Rx) Noise power Path loss (cell link) Path loss (D2D link, NLOS) Path loss (D2D link, LOS) Shadowing std. RB size Sub-carrier spacing BLER target TTI

Main Simulation Parameters Value 500 m Type 2 (TDD) 0 2.5 GHz 46 dBm 20 dBm BS: 14 dBi; Device: Omni directional 0 dBi -174 dBm/Hz 128.1 + 37.6 log(d), d[km] 40 log(d) + 30 log(f) + 49, d[km], f[Hz] 16.9 log(d) + 20 log (f/5) + 46.8, d[m], f[GHz] 10 dB (cell mode); 12 dB (D2D mode) 12 sub-carriers, 0.5 ms 15 kHz 10% 1 ms

In the first analysis, the focus is on Scenario A and both the mean data rate achieved by multicast users and the Aggregate Data Rate (ADR), i.e., the sum of the individual data rates, are measured. The results are plotted in Fig. 2. As expected, both mean data rate and ADR increase with the number of available RBs for all solutions. The D2 -CMS always outperforms the CMS, with a gain that increases with the number of available RBs. In fact, the gain is equal to 44% for 10 RBs, but it reaches 69% for 100 RBs. In particular, the mean data rate for the CMS varies between 0.25 and 2.5 Mbps, whereas for the D2 -CMS it is between 0.36 and 4.24 Mbps, as shown in Fig. 2(a). When considering the OMS performance, the data rate varies between 0.75 and 7.5 Mbps, showing always the highest values among the three solutions. The price to pay is a corresponding reduction in terms of number of served users and fairness, as shown in the following. A similar trend can be observed for the ADR in Fig. 2(b). In particular, the minimum ADR values are 50, 72, and 150 Mbps, for CMS, D2 -CMS and OMS respectively, whereas the maximum values are equal to 502, 850, and 1500 Mbps. Focusing now on Scenario B, the impact of the multicast group size is highlighted in plots in Fig. 3. Also for these cases, the D2 -CMS outperforms the CMS. For all the solutions the mean data rate slightly decreases when the number of users in the cell increases, Fig. 3(a). In particular, the performance of CMS varies from 3.7 with 20 multicast users to 2.5 Mbps with

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(a) Mean data rate Fig. 2.

(b) Aggregate Data Rate

Performance in Scenario A. Fig. 4.

200 UEs, with a reduction of about 32%. When considering the D2 -CMS, the mean data rate decreases from 5.7 Mbps with the smallest group size to 4.24 Mbps with the largest group size, with a consequent 25% reduction. This is an expected result as the more users are in the group the higher is the chance of having users with very bad channel conditions. While also the OMS has a reduction in the mean data rate with more UEs, this phenomenon has less impact by decreasing from 7.9 Mbps with 20 UEs to 7.5 Mbps with 200 UEs. When looking at the ADR, as expected, it grows when the number of multicast members in the cell increases. Moreover, the gain introduced by the D2 -CMS w.r.t the CMS solution is larger when the number of multicast users increases, varying between 56% and 67% in the considered range. In details, the ADR for the CMS varies from 73 Mbps to 502 Mbps, for the D2 -CMS it varies between 114 Mbps and 842 Mbps, while for OMS the ADR varies between 157 Mbps and 1500 Mbps, for the 20 UEs and the 200 UEs cases respectively.

(a) Mean data rate Fig. 3.

the same data rate. The values reported in Table IV clearly show that OMS cannot guarantee service to all users on a short term basis. Indeed, the percentage of served UEs is equal to 60%, while, as expected, it is equal to 100% for both the CMS and the D2 -CMS solutions. Looking at the number of UEs served by the proposed D2 -CMS solution on either the downlink or the D2D links, 89% (178 of the 200 UEs) of multicast users are served through the cellular link, while the remaining UEs are served through D2D links, by using on average 4 relays. A nice example of service configuration when the D2 -CMS solution is implemented is plotted in Fig. 4, where the role of each UE in the group is highlighted in the reference cell-edge scenario. A further aspect to be underlined is that the exploitation of D2D links through the proposed D2 -CMS policy allows offloading the downlink resources compared to both CMS and OMS approaches. This is an important result as the downlink channel congestion is a well-known issue in cellular systems, and the resources that have not been used in downlink can be used for additional services in the cell. In particular, the D2 -CMS solution guarantees a reduction in terms of occupied RBs in downlink of about 32%. Finally, in Table IV it can be observed that the D2 CMS solution significantly enhances the data rate, but without negatively affecting the fairness. On the contrary, although the OMS can offer even higher data rate performance, the price to pay is in terms of fairness and number of served UEs.

(b) Aggregate Data Rate

Performance in Scenario B.

A further analysis is presented in Table IV, where some interesting performance figures are summarized for a sample study case with R = 100 RBs and N = 200 UEs (similar results and analysis can be obtained under different sample cases). The measured parameters are (i) the percentage of served UEs in the multicast group, (ii) the relationship between the number of UEs served in downlink and on D2D links, (iii) the number of relay nodes, (iv) the relationship between the RBs used in downlink and in D2D links, and (v) the short-run fairness in the data rate assignment, computed according to the Jain’s Fairness Index (FI) [19]: P 2 ( |N| i=1 di ) FI = P|N| 2 |N|( i=1 di )

Sample configuration for D2 -CMS (200 UEs, 100 RBs).

(2)

where di is the total data rate for UE i; FI = 1 is the maximum fairness value that is achieved when all UEs are served with

TABLE IV.

Performance Results (200 UEs, 100 RBs).

Served UEs [%] N cell /N D2D Nr Rdl /Rul FI

CMS 100 200/0 100/0 1

D2 -CMS 100 178.33/21.67 4.2 68.6/31.4 1

OMS 60 119.62/0 100/0 0.78

To conclude the analysis presented in this paper, we focus on a wide set of UEs distributions within the cell. To do this, the CMS solution is used as a benchmark of minimum performance in the tested scenarios. The area where the UEs are uniformly distributed is progressively extended from the cell-edge scenario until the whole cell is covered. In details, the average data rate gains, w.r.t. the CMS, introduced by the D2 -CMS scheme are plotted in Fig. 5 for a sample value of available RBs R = 100 and a variable number of UEs. Focusing

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the attention on the number of UEs in the MG (x-axis in the plots) and the area covered within the cell MG area (the y-axis in the plots reports the side length of the considered square area), the gain increases with the number of users and decreases with the area size. This is an expected behavior as the D2D coverage range is limited to a maximum of 50 m and larger areas with the same number of UEs reduce the possibility to exploit D2D links.

[6]

[7]

[8]

[9] [10]

[11]

[12]

[13]

[14] Fig. 5.

Data rate gain for

D2 -CMS

vs. CMS. [15]

V.

Conclusions

This paper introduces significant enhancements to the standard CMS solution in terms of efficient delivery of multicast services in LTE-A networks. The proposed solution is based on the exploitation of D2D links between UEs within the multicast group. The proposed D2 -CMS scheme is designed to dynamically activate D2D links in order to select the “best” relay nodes and minimizing the number of activated relays. The proposed solution outperforms the CMS solution in terms of data rate in all the considered scenarios, and the gain increases with the number of multicast users in the cell and the number of available RBs. Moreover, the improvements are achieved without affecting the service coverage and the fairness among the multicast members. The proposed D2 -CMS solution reduces the number of resources necessary for multicast service delivery in the downlink direction, with a consequent downlink channel offloading. Note that, frequency resources reuse on the D2D links have not been considered in this work, but these would actually allow for further improvements in the performances. The analysis integrating these techniques will be the focus of future work. References [1]

3GPP, “TS 36.300, Evolved Universal Terrestrial Radio Access (EUTRA) and Evolved Universal Terrestrial Radio Access Network (EUTRAN), Rel. 11,” Tech. Rep., Sept. 2012. [2] A. Richard, A. Dadlani, and K. Kim, “Multicast scheduling and resource allocation algorithms for OFDMA-based systems: A survey,” IEEE Communications Surveys and Tutorials, vol. 15, no. 1, 2013. [3] 3GPP, “Specification Group Services and System Aspects; Multimedia Broadcast/Multicast Service (MBMS); Architecture and functional description, Rel. 11,” Tech. Rep., Sept. 2012. [4] T. P. Low, M. O. Pun, Y. W. P. Hong, and C. C. J. Kuo, “Optimized opportunistic multicast scheduling (OMS) over wireless cellular networks,” IEEE Trans. on Wireless Communications, vol. 9, no. 2, Sept. 2009. [5] G. Araniti, M. Condoluci, and A. Iera, “Adaptive multicast scheduling for HSDPA networks in mobile scenarios,” IEEE BMSB, pp. 1–5, June 2012.

[16]

[17]

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