Multi-Radio Heterogeneous Networks: Architectures and Performance. Nageen Himayat, Shu-ping Yeh, Ali Y. Panah, Shilpa Talwar, Intel Corporation.
2014 International Conference on Computing, Networking and Communications, Invited Position Papers
Multi-Radio Heterogeneous Networks: Architectures and Performance Nageen Himayat, Shu-ping Yeh, Ali Y. Panah, Shilpa Talwar, Intel Corporation {nageen.himayat, shu-ping.yeh, ali.yazdan.panah, shilpa.talwar}@intel.com Mikhail Gerasimenko, Sergey Andreev, Yevgeni Koucheryavy, Tampere University of Technology {mikhail.gerasimenko; sergey.andreev; yk}@tut.fi ABSTRACT It is well-known that next generation (5G) wireless networks will need to provide orders of magnitude more capacity to address the predicted growth in mobile traffic demand, as well as support reliable connectivity for billions of diverse devices, which comprise the “Internet of Things.” While the industry is focused on a concerted effort to improve capacity of cellular networks, operators are increasingly using WiFi technology over un-licensed spectrum to relieve congestion in their networks. This paper argues that the trend towards integrated use of multiple radio access technologies (RATs) and networks, such as WiFi, will be essential for addressing the challenges faced by future 5G networks. In particular we expect that joint use of multiple RATs can yield beyond additive gains in user connectivity experience by exploiting the rich multi-dimensional diversity (e.g., spatial, temporal, frequency, load, etc.) available across multiple radio networks. We investigate such benefits through a case study on integrating WiFi with 3GPP heterogeneous networks. Our results show that intelligent integration of WiFi/3GPP radio networks can yield an additional 2-3x gains in system capacity and user quality of service, beyond what is achievable from independent use of both networks. Index Terms—Multi-radio Networks, Heterogeneous Networks, 3GPP, Long Term Evolution (LTE), WiFi, 5G, 802.11, Interference Management, Integrated Small Cells, Spectrum Aggregation, WiFi Offload, Multi-RAT Architectures, Cross-RAT cooperation. I. INTRODUCTION Heterogeneous networks (Het-Nets) have emerged as one of the key network architectures for addressing the aggressive capacity and coverage demands of future 5G networks. This architecture comprises hierarchical, multi-tier deployment of cells with different footprints, which can potentially operate over multiple radio access technologies (RATs). Typical deployments comprise an overlay of a macro-cell network with additional tiers of densely deployed small cells, such as picos, femtos, relay nodes, WiFi access points etc. Significant network capacity gain can be achieved at low cost, not only through aggressive reuse of spectrum across the multiple tiers in the network, but also through harnessing
978-1-4799-2358-8/14/$31.00 ©2014 IEEE
additional spectrum in un-licensed bands by integrating WiFi in the network. A comprehensive overview of multi-tier, multi-radio heterogeneous networks is provided in [1]. Figure 1 illustrates our vision of a Multi-RAT Het-Net deployment. The trend towards the use of WiFi in conjunction with cellular networks has grown from a need to relieve congestion on cellular networks as new devices for rich multi-media consumption emerged. Today, a large percentage of consumer devices support WiFi along with additional radio technologies. Recent WiFi standards also support increasingly higher data rates utilizing greater number of frequency bands. WiFi is also attractive to operators from a cost point of view. Therefore, WiFi has become an integral part of their strategy to address the capacity limitations of future networks. Although the use of WiFi offload has become popular in recent years, the cellular standards (3GPP: Third Generation Partnership Project) community has been engaged in developing specifications that consider interworking between the cellular as well as WLAN (Wireless Local Area Networks) [2] for a number of years. These efforts have now accelerated. Several new study and work items are currently in progress, which continue to develop specifications towards increased integration of WiFi with cellular networks [2, 4]. While most of the effort has focused on loose interworking solutions requiring changes within the core network to improve security and inter-RAT mobility with WiFi networks, recent work also addresses interworking within the Radio Access Network (RAN). This shift is guided by the need to support better QoS on unlicensed spectrum as prescribed by a consortium of network operators who have put together aggressive requirements for carrier grade WiFi. The WiFi community has also responded with new initiatives such as Hot Spot 2.0, as well as a new “High Efficiency WLAN” standardization effort in IEEE 802.11 working group [5, 6]. In our view, capacity and connectivity limitations faced by future networks will continue to drive the need to not only improve the performance of individual RATs but also for tighter integration of multiple RATs, encompassing other technologies beyond WiFi and
252
2014 International Conference on Computing, Networking and Communications, Invited Position Papers
additional use cases beyond spectrum aggregation. Our view is based on the following considerations. Spectrum Aggregation Given the limited licensed spectrum, there will be ongoing momentum to harness capacity across multiple RATs. Even when new spectrum is allocated, the allocations are likely to be fragmented and could require different transmission technologies (air interface descriptions) for use. Hence there is a need to aggregate different radio technologies as part of a common virtual radio network, in a manner transparent to the end user, and develop techniques that can efficiently utilize the radio resources available across different spectral bands potentially using different RATs. Convergence of LAN, PAN and WAN As cell-sizes shrink, the footprints of cellular wide area networks (WANs), local area networks (LANs), and personal area networks (PANs) will increasingly overlap (e.g. the overlap in the footprints of 3GPP and unlicensed spectrum networks with the shift towards smaller cells). This creates an opportunity to simultaneously utilize multiple RATs for improving wireless connectivity. Connectivity Needs of Internet of Things The emerging Internet of Things (IoT) will require networks to connect a diverse range of devices requiring connectivity at different scales. Interworking across the multiple RATs that are optimized for each scale of connectivity will therefore become essential. For example, current smart phone devices can provide cellular „hotspot‟ connectivity for WiFi-equipped peripherals such as tablets, cameras, etc. Such features will need to be extended for IoT applications. Increased Cooperation in the Network Increased cooperation across network nodes will be required to achieve capacity gains that scale linearly with device density [7]. In particular, a mix of short- and long-range technologies will need to work cooperatively to realize such linear scaling. For example devices may cooperate or communicate over a PAN technology locally to improve connectivity over a wide area network link. Therefore we believe that multiple RATs and the associated device/system intelligence for their efficient use will be a fundamental characteristic of future networks. In particular, we expect that joint use of multiple RATs can leverage the rich multi-dimensional diversity (e.g., spatial, temporal, frequency, interference, load, etc.) across multiple radio networks (WAN, LAN and PAN) to provide beyond-additive gains in network capacity and user connectivity experience. In this paper we explore the benefits of multi-RAT integration using the example of integrating WiFi within 3GPP Het-Nets as a case study. Specifically, we consider
the benefits of tighter radio layer integration and coordination between WiFi and LTE air interfaces, by leveraging multi-RAT capable small cells. Multi-RAT small cells with co-located WiFi and LTE interfaces are an emerging industry trend, as they help reduce deployment costs by leveraging common infrastructure across RATs. The co-located radios on the base station allow for tighter coordination across the multiple radio links, when used together with multi-radio client devices. In this integrated architecture, we may consider WiFi as a “virtual carrier” in the overall cellular network, allowing tighter integration across WiFi and cellular networks. The increased coupling and coordination across multiple RATs can be leveraged in several different ways. In this paper, we present joint RAT assignment, selection and scheduling algorithms, which provide significant gains in overall system performance and user quality of service (QoS). In particular we highlight gains in interference management and avoidance, as well as improvement in QoS for delay sensitive traffic focusing on the “timely throughput” metric [8]. Some of the results presented here have been covered in [9, 12], but new results on dynamic coordination in the presence of WiFi interference are presented for the first time. While our investigation focuses on the Downlink performance, we also discuss issues and extensions needed for the Uplink. This paper is organized as follows: Section II discusses available architectural alternatives for WiFi-LTE integration and points out benefits of integration within the Radio Access Network (RAN). In Section III, we review various algorithms and associated performance benefits of joint RAT coordination. Performance results are discussed in Section IV. Section V summarizes the paper and discusses future implications of our study. II. ARCHITECTURAL CONSIDERATIONS FOR MULTI-RAT INTEGRATION
Several key questions must be answered when considering multi-RAT integration options. For example, it is important to address a) How are RATs discovered? b) How does RAT selection or assignment happen? c) Who makes the selection decisions? d) How are sessions transferred across RATs? etc. Figure 2 highlights several different architectural choices for integrating WiFi and LTE networks, which answer such questions differently. As shown in option A, the application on the user device and the content server can communicate directly, over potentially a proprietary interface, and exchange data over multiple RATs, without any coordination within the network. While such a choice is beneficial for supporting user quality of
253
2014 International Conference on Computing, Networking and Communications, Invited Position Papers
experience (QoE), it is application dependent and may ignore the underlying network conditions.
a multi-RAT cell in a Het-Net environment, and local per cell inter-RAT coordination is used to serve users.
Current solutions for WiFi/3GPP integration rely on lightly coupled integration in the core network (option B), wherein an Access Network Discovery & Selection function (ANDSF) assists the user (UE) in discovering WiFi APs as well as provides policies for network selection. The final network selection decision is left to the user which combines knowledge of its local radio link conditions with operator policies available at the user. Seamless Inter-RAT session transfers are accomplished via higher layer signaling protocols, such as the IP Flow Mobility protocol based on mobile-IP variants. There are several advantages of this approach as it can properly account for both operator policies and user preferences. However, the performance of such schemes may still be limited due to the fact that UEs only have local knowledge of the wireless network conditions [10]. While the UE can report its varying radio link information to the core network, such information exchange can incur high overhead. Here radio network assistance and control may be required to support QoS with dynamically changing radio link conditions.
III. PERFORMANCE BENEFITS
Therefore, it is of interest to explore integration options within the Radio Access Network (RAN), where network wide knowledge of radio link information is available. Cross-RAT information exchange across base-stations and WiFi APs may be available through UE assistance or a suitable interface may be defined between WLAN and 3GPP infrastructure. With integrated multi-RAT small cells, full cooperation across multiple RATs may become available, allowing for more dynamic radio resource management for improved system and user performance. Further, the 3GPP RAT can also be used as a mobility and control anchor, wherein a user utilizes 3GPP protocols for transferring sessions to a multi-radio cells and then use local switching to transfer sessions to and from WiFi with low latency. There are several advantages to such an approach as adaptations to dynamic variations in interference conditions can easily be made without significant session interruptions and packet loss. In [11], we show that even simple assistance information (such as network load information) from the radio network can help improve the offload performance of UE-based network selection schemes. In the next section we investigate the benefits of a fully cooperative multi-RAT architecture available through the use of multi-RAT small cells. Specifically, we assume a virtual RAN architecture where WiFi serves as a “virtual” data carrier in the 3GPP network. Here existing 3GPP protocols are used for association and session transfer to
This section considers several techniques for exploiting the cross-RAT coordination available with the Virtual RAN architecture enabled by integrated LTE-WiFi small cells. As a reference we assume a baseline case where there is no cooperation across the LTE and the WiFi radios, and the two links schedule their transmissions independently without exchanging any information. In another cooperative model, a multi-RAT interworking function can be used to monitor the quality of radio links across users and the users (or data flows) can be better assigned across RATs to satisfy user QoS requirements. Typically such assignments will be made in a semi static fashion, wherein they are updated infrequently during the course of a session to avoid expensive signaling for inter-RAT session transfers. Fast session transfers between co-located Rats allow for such assignments to change dynamically. Enabling dynamic RAT assignments with virtual RAN architectures can be beneficial in improving system reliability and user QoS, particularly in the presence of uncoordinated interference over unlicensed WiFi bands. Tighter cooperation involving joint RAT scheduling is another option, which requires MAC layer coordination across RATs. In this case, a MAC scheduler selects across all associated users based on its scheduling rule and a user may simultaneously transmit over multiple radios if scheduled to do so by the cross-RAT MAC scheduler. Dynamic scheduling may provide benefits especially in tracking fast fading and rapidly varying interference conditions. In this paper, we evaluate both RAT assignment and cross-RAT scheduling approaches. A. Coordinated RAT Assignment A RAT assignment framework was developed for optimizing proportional fair and on-time throughput metrics in [9]. The main idea behind this framework is to assign users to cells or RATs such that proportional fair or product of user utilities is maximized. While more general utility functions can be defined, [9] considered average and on-time throughput as example utility functions. Given that conventional 3GPP based cell association methods were used to associate users with a multi-RAT small cell, the assignment problem simply became a problem of partitioning users across the WiFi and 3GPP RATs. When the number of users associated with the small cell is low, this partitioning of users may be solved optimally through exhaustive search. However, for the special case of proportional fair throughput metric, the partitioning was shown to be optimally solved by rank ordering the ratios of WiFi and
254
2014 International Conference on Computing, Networking and Communications, Invited Position Papers
LTE rates and comparing them to a threshold that was based on the optimal split of users between the WiFi and LTE RATs. Simpler user partitioning algorithms for maximizing the on-time throughput metric that do not require exhaustive search were also considered in [12]. A brief summary is included in the following for clarity. On-time throughput is a useful metric for assessing the capability of a system to support delay sensitive traffic. It may be calculated by dividing time into slots of a fixed duration, then assessing whether the required number of packets dictated by the target on-time delivery rate is received in each time slot. The average on-time throughput of user is then defined as the probability that a packet with the specified delay constraint is successfully received within every time-slot. The effective goodput (throughput delivered before the delivery deadline) is then the throughput weighted by the proportion of time slots where the target data throughput is received on-time. Keeping this in mind, a lower complexity RAT assignment technique can partition users across WiFi & LTE such that minimum amount of resources are required across both the LTE and the WiFi links to support associated users in achieving their target on-time throughput. Reference [9] covered RAT assignments that were relatively static and therefore could not respond quickly to uncoordinated WiFi interference. A more dynamic assignment scheme allows for regular adjustments to user RAT assignments based on a periodic update period. Accordingly, WiFi and LTE throughput estimates must also updated on a regular basis in contrast to the static assignment case where an initial measurement period may be assumed to generate the estimates. B. Cross-RAT Scheduling With integrated WiFi-LTE small cells, cooperation at the MAC layer and joint scheduling of LTE and WiFi resources also becomes feasible. Here, the joint scheduler manages and schedules radio resources of both LTE and WiFi links. It monitors both radio links and keeps track of the aggregated throughput, ongoing transmissions that are in-complete and the unacknowledged packets. Cross-RAT scheduling can be particularly helpful when dealing with delay sensitive traffic that requires tighter management of packets to avoid excessive latency. Hence, in this paper we consider cross-RAT scheduling in the context of maximizing on-time throughput for users. To make a fair assessment of the gains in on-time throughput achievable with cross-RAT scheduling, a per RAT scheduling algorithm must also be designed to optimize on-time throughput. Reference [12], describes such a scheduling algorithm. To maximize the effective
goodput, measuring the number of packets delivered to a user before the delay deadline, the scheduling algorithm in [12] specifically seeks to increase the number of users receiving data throughput in excess of their targeted timely throughput. For example, no additional resources are allocated to users who have already received more than the required number of bits within the current time. The scheduler, thus, only scans through users receiving less than the target number of bits within the current time slot and selects the one who is most likely to achieve the target throughput while using the minimal amount of resources. Such a scheduling algorithm is shown to significantly out-perform scheduling based on maximizing proportional fair throughput [12]. IV. PERFORMANCE EVALUATION A. Downlink Performance Results This section compares the relative performance of the various RAT assignment and scheduling schemes discussed in Section III. In [9] we considered the benefits of coordinated RAT assignments in exploiting WiFi to lower interference in 3GPP Het-Nets. A gain of 2-3x in system throughput and cell-edge performance was observed without compromising the macro-cell performance. Here we consider the performance of dynamic link assignments to improve system outage in the presence of uncoordinated WiFi interference. Figure 3 compares the system performance of static and dynamic RAT assignments in the presence of rogue or uncoordinated WiFi interference. It can be seen that system performance across both schemes degrades in the presence of rogue WiFi interference as the WiFi capacity gets shared across the interferers as prescribed by WiFi‟s random-access mechanism. However, the dynamic link assignment scheme can improve system outage compared to static schemes (over 5x improvement is observed for the simulated scenario). We further note that in our simulations the update period of at least 200 milliseconds was required to achieve the target outage improvement, pointing to the need for faster inter RAT switching mechanisms, such as those supported by the virtual multi-RAT architectures Next we consider QoS improvements feasible with integrated multi-RAT architectures, focusing on the on-time throughput metric as the QoS metric indicative of performance of delay sensitive applications. Figure 4 compares the performance of assignment and scheduling algorithms in the context of maximizing on-time throughput. The results shown build upon scheduling algorithms that maximize on-time throughput. Five different cases are shown in the figure. The first two cases are given for reference and consider the single radio case using only LTE or WiFi radios. The last 3
255
2014 International Conference on Computing, Networking and Communications, Invited Position Papers
cases require co-located LTE and WiFi radios and cover techniques for different levels of cooperation between LTE and WiFi radios: 1) independent operation, 2) cooperative user assignment across RATs, and 3) cooperative user scheduling on both RATs. It can also be seen from Figure 4 that increased cooperation across RATs can improve the number of users achieving their target on-time throughput. In particular, cross-RAT scheduling can service up to 3 times more users at the target on-time rate of 6.93 Mbps (see simulation assumptions). B. Uplink Performance Considerations In the past section we have illustrated the benefits of a virtual RAN architecture using Downlink performance results. Uplink performance results are also an important consideration especially as the Uplink interference environment is highly dynamic and depends on user distribution, user scheduling and RAT assignment decisions. Here the active users determine the interference and contention level on the 3GPP and WiFi networks respectively. Therefore we expect the performance gains with a network controlled architecture to be more substantial. Reference [11] illustrated the benefits of RAN assistance in improving performance of UE based network selection without assuming any network layer cooperation across RATs. Further work will investigate the benefit of increased cooperation enabled with the virtual RAN architecture. V. SUMMARY & CONCLUSIONS In this paper we argue for increasingly integrated multi-RAT network deployments. Various architectural options for such deployments are discussed. In particular we highlight the benefits of integrated multi-RAT architectures where all RATs are managed as part of a single virtual radio access network allowing for full cooperation across RATs within the radio network. Using WiFi and LTE integration as a case study, we demonstrate several performance benefits of the virtual multi-RAT access network architecture. In particular we showed significant gains in system outage probability when WiFi performance was degraded by uncoordinated interference. We further showed up to 3x gains in number of users achieving their target QoS measured in terms of on-time throughput. Our results illustrating the promise of cross-RAT cooperation, coupled with the simplicity in deploying multi-RAT small cells, suggest that integrated virtual RAN architectures will play an important role in future deployment of 5G multi-RAT networks. We also expect to see continued momentum towards tighter integration and management of multiple RATs within future networks. However, more work is needed to combine
user and operator preferences and policies as part of such architectures. Further, achieving the benefits of cross-RAT integration with distributed multi-RAT deployments remains an area of exploration. Uplink performance must also be better characterized. ACKNOWLEDGEMENTS We acknowledge V. Gupta, K. Etemad, H. Yin, S. Sirotkin, M. Fong, and K. Johnsson at Intel for helpful discussions. We also thank W.C. Wong, Y. Zhu, T. Papathanassiou, H. Sherani-Meher, and E. Perahia for their help with the multi-RAT system level simulator. Part of this work has been supported by the Internet of Things program of Tivit (funded by Tekes). APPENDIX: EVALUATION METHODOLOGY In this section we describe the Multi-RAT system evaluation methodology to assess the performance of the scheduling and cross-RAT coordination mechanisms discussed earlier. Specifically, we extend the 3GPP evaluation methodology for heterogeneous deployments [13-14], to model the contention-based WiFi MAC. Specifically, we focus on out-door small cell deployments, as this scenario results in the most challenging interference environment. The small cells are randomly overlaid on a macro-cell deployment of 500 meter inter-site distance, and users are clustered around the small cells according to the procedure described in [13]. We use a dynamic simulation model that utilizes fast link adaptation and frequency selective scheduling to react to fast channel variations. Table 1 describes the simulation assumptions. Rogue WiFi interference is modeled as additional WiFi transmitters that start transmitting on the same WiFi frequency used by the operator deployed access points. The interference is turned on and off during a simulation run. To evaluate the on-time throughput performance, we target the requirements induced by a video application stream running in real-time with =30ms and =0.20787 Mbits. The maximum achievable effective goodput for this stream is 6.93Mbps. VI. REFERENCES 1.
Yeh, S. Talwar, G. Wu, N. Himayat, K. Johnsson, “Capacity and coverage enhancement in heterogeneous networks,” invited paper, pp. 32-38, IEEE Wireless Comm. Magazine, June, 2011.
2.
3GPP R2-130588, “Overview of existing WLAN related 3GPP specifications and on-going work,” January, 2012.
3.
3GPP RP122038, “WLAN/3GPP Interworking,” December, 2012.
256
Radio
2014 International Conference on Computing, Networking and Communications, Invited Position Papers
4.
3GPP TR 37.864, “3GPP/WLAN Interworking,” May, 2013.
5.
Scheduler 3GPP RP-130435, “P-HEVOR: Systemsthroughput maximizin requirements and architecture by the NGMN Scheduling granularity Traffic load Alliance,” May, 2013.
6. 7.
8.
9.
IEEE 802.11-13-0331-r5, WLAN,” March, 2013.
“High
RAN
Efficiency
A. Ozgur, O. Leveque and D. N. C. Tse, “Hierarchical cooperation achieves optimal capacity scaling in ad -hoc networks,” IEEE Trans. on Inf. Theory, vo. 53, 2007. Sina Lashgari and A. Salman Avestimehr. “Timely Throughput of Heterogeneous Wireless Networks: Fundamental Limits and Algorithms,” (arXiv:1201.5173v1) submitted to IEEE Trans. on Information Theory, Jan. 2012. A. Y. Panah, S.-P. Yeh, N. Himayat, and S. Talwar, “Utility-based radio link assignment in multi-radio heterogeneous networks,” in IEEE Globecom Workshop on Emerging Technologies for LTE-Advanced and Beyond 4G, Dec. 2012.
UE channel estimation Feedback/control channel errors
Receiver type Feedback periodicity CQI & PMI feedback granularity in frequency
Ideal No Error Prop. Fair, Max. On-time 5 PRBs Full buffer for both WiFi & LTE Interference unaware MMSE 10ms 5 PRBs
PMI feedback
3GPP Rel.-10 LTE codebook (per sub-band)
Outer loop for target FER control
10% PER for 1st transmission
Link adaptation HARQ scheme WiFi Parameters WiFi Frequency/Channel Number of frequency bands MPDU Size Rogue Interferers
10. E. Aryafar, A. Keshavarz-Haddad, M. Wang, and M. Chiang, “RAT selection games in Het-Nets,” in Proc. of IEEE INFOCOM, April, 2013. 11. M. Gerasimenko, N. Himayat, S. Yeh, S. Talwar, S. Andreev, Y. Koucheryavy, “Characterizing performance of load ware network selection in multi-radio (3GPP/WiFi) heterogeneous networks,” submitted to IEEE Globecom workshop on Broadband Wireless Access, 2013.
MCSs based on LTE transport Format CC WiFi 802.11g, Same network deployment as LTE 2.4 GHz band/20 MHz 3 1500 Bytes 3 rogue APs /sector
Integrated LTE-WiFi BS
WiFi-AP
Pico-BS
Femto-AP
12. S. Yeh, A. Y. Panah, N. Himayat, and S. Talwar, “QoS aware scheduling and cross-RAT coordination in multi-radio heterogeneous networks,” To appear in IEEE VTC, Sep. , 2013. 13. 3GPP TR 3GPP 36.814, “Further advancement of EUTRA physical aspects, 2011. 14. TR 36.819, “Coordinated multi-point transmission for LTE physical layer aspects,” v. 11.1, 2011. Table 1: Simulation Assumptions
(LTE/WiFi)
Figure 1: Multi-RAT Het-Nets: An emerging cellular network topology.
LTE 1 pico/sector, 9 UE/sector , Topology 19 cell wrap-around RSRP bias, ABS 0dB, 0% UE dropping clustered (config. 4b) [IMT] UMa Macro, UMi Pico, Channel/UE speed UE speed= 3 km/hr LTE mode Downlink FDD @ 10 MHz No. antennas (macro, pico, UE) (2, 2, 2) macro, pico: co-polarized, Antenna configuration UE: co-polarized (||-->||) Max rank per UE 2 (SU-MIMO)
257
2014 International Conference on Computing, Networking and Communications, Invited Position Papers
Figure 2: Possible integration architectures for interworking between WiFi & LTE Percentage of users achieving “on-time” throughput
Multi-RAT Scheduling
Throughput & outage in the presence of WiFi interference
Static RAT Assignment WiFi Interference On/Off Outage (%)
Off
On
0% 8.2% Throughput Results
Dynamic RAT Switching Off
WiFi+ LTE No Coordination
On
0
1%
Cell-Edge (Mbps)
3.2
0
3.97
0.64
Total (Mbps)
55.4
35.3
53.7
34.2
WiFi LTE Low-complexity RAT Assignment
~3x
Optimal RAT Assignment
WiFi Interference degrades system
& cell-edge throughput. Minimum cell-edge throughput is preserved w/ dynamic RAT assignment
On-Time Throughput (bits/s)
Multi-RAT scheduling significantly improves on-time throughput at high QoS requirements. Coordinated Multi-
Dynamic RAT assignment w/ 3GPP control restores system outage when WiFi link quality drops due to interference .
RAT assignment also performs well.
Figure 3: Outage improvement w/ dynamic interference avoidance & inter-RAT switching.
Figure 4: On-time throughput performance for different cross-RAT coordination techniques [12].
258
