What is the Role of MIMO in Future Cellular Networks: Massive? Coordinated? mmwave?

Transcription

1 What is the Role of MIMO in Future Cellular Networks: Massive? Coordinated? mmwave? Robert W. Heath Jr. The University of Texas at Austin Wireless Networking and Communications Group Presentation

2 Wireless is Big in Texas 20 Faculty 12 Industrial Affiliates Affiliates champion large federal proposals, provide technical input/feedback, unrestricted gi: funds WNCG provides pre- prints, pre- research ideas, vast first access to students About half of all students intern for an affiliate or work Affiliates provide real world context 150 Grad Students

3 Wireless Communications Lab front.pdf 1 9/12/11 4:46 PM Undergrad/grad lab course QAM & OFDM experiments Complete lab manual & software Uses USRP equipment LabVIEW programming Complete lab manual available DIGITAL COMMUNICATIONS PHYSICAL LAYER EXPLORATION LAB USING THE NI USRP PLATFORM Dr. Robert W. Heath, University of Texas at Austin 3

4 Outline MIMO in cellular networks Coordinated Multipoint a.k.a. network MIMO Massive MIMO Millimeter wave MIMO Comparison between technologies Parting thoughts 4

5 The MIMO Concept H Transmitter Receiver multiple TX antennas multiple inputs propagation channel multiple outputs multiple RX antennas MIMO (multiple-input multiple-output) communication channel Leverages multiple antennas at the transmitter and receiver MIMO is broadly incorporated into wireless systems Cellular communication, wireless local area networks, ad hoc networks Provides capacity, quality, robustness, resilience to interference MIMO communication exploits matrix propagation channels 5

6 Point-to-Point MIMO 2 data streams # of parallel data streams is the multiplexing gain Single user MIMO communication Send multiple data streams a.k.a. spatial multiplexing Highest performance requires rich scattering environment Incorporated into several commercial wireless systems Ex: 8 antennas at the base station and 4 antennas at the mobile station Multiplexing gains are limited by number of antennas at users 6

7 Point-to-Multipoint MIMO 2 data streams 2 data streams 4 streams total Multiuser MIMO (MU-MIMO) Simultaneously send independent data streams to several users Multiplexing gains are obtained even with less ideal propagation Main obstacles: near-far problem & channel correlation Flexibility in user scheduling may overcome the obstacles Multiplexing gains are limited by number of antennas at base stations 7

8 Where is MIMO Headed? Coordinated MIMO B Massive MIMO mmwave MIMO Candidate architectures for 5G 8

9 Outline MIMO in cellular networks Coordinated Multipoint a.k.a. network MIMO Massive MIMO Millimeter wave MIMO Comparison between technologies Parting thoughts 9

10 What is Network MIMO? Out-of-cell interference cellular backhaul network Coordinated transmission from multiple base stations Known as CoMP or Cooperative MIMO or base station coordination Interference turned from foe to friend Exploits presence of a good backhaul connection Used to improve area spectral efficiency, system capacity 10

11 Network MIMO Architectures enodeb Coordinate over backhaul enodeb enodeb Like DAS uses local coordination Cloud RAN Processing for many base stations using cloud Coordination clusters Dynamic coordination 11

12 Potential Gains from Coordination cells and 3 sectors per cell BS-MS distance= 192 m ISD=500m BS MS K=1 K=3 K=9 sum rates per cell Sum-rates (bits/sec/hz) Linear increase Unbounded gains Grid model for a cellular network Throughput gains when out-of-cluster interference is ignored More cooperation leads to higher gains SNR (db) Cell edge pushed further out, no uncoordinated interference in the cell 12

13 Addressing Out-of-Cell Interference cells and 3 sectors per cell BS-MS distance= 192 m ISD=500m BS MS K=1 K=3 K= Sum-rates (bits/sec/hz) Sum-rates saturation Bounded gains Grid model for a cellular network SNR (db) Performance saturates with out-of-cluster interference 30% performance gains observed in industrial settings Be mindful of the saturation point A. Lozano, R. W. Heath, Jr., and J. G. Andrews, ``Fundamental Limits of Cooperation" to appear in the IEEE Trans. on Info. Theory. Available on ArXiv. 13

14 Critical Issues with Network MIMO Out-of-cell interference When included, results are not as good Feedback overhead Need channel state information Performance seriously degrades Control channel overhead Increased reference signal overhead Backhaul link constraints Backhaul link latency (delayed CSI and data sharing) Cluster edge effects channel state feedback overhead enodeb enodeb Backhaul delay Reference signal Data payload Coordination still has a cell edge with fixed clusters Dynamic clustering solves the problem, but more more implementation overhead 14

15 Network MIMO Conclusions Observations Network MIMO promises a way to get rid of interference...yet uncoordinated interference still limits high SNR performance Backhaul constraints and system overheads further reduce performance gains General disconnect between academia and industry on the potential Forecast Already incorporated into 4G, coordination will be part of 5G as well Architectures will evolve to support network MIMO-like coordination Distributed antenna systems are a good starting point Distributed radio access networks are a likely evolution point Cloud radio access networks are a possible end objective 15

16 Outline MIMO in cellular networks Coordinated Multipoint a.k.a. network MIMO Massive MIMO Millimeter wave MIMO Comparison between technologies Parting thoughts 16

17 What is Massive MIMO? hundreds of BS antennas tens of users A very large antenna array at each base station An order of magnitude more antenna elements in conventional systems A large number of users are served simultaneously An excess of base station (BS) antennas Essentially multiuser MIMO with lots of base station antennas T. L. Marzetta, Noncooperative cellular wireless with unlimited numbers of base station antennas, IEEE Trans. Wireless Commun., vol. 9, no. 11, pp , Nov

18 Massive MIMO Key Features Uplink Downlink training channel pilot reciprocity contamination interference asymptotic orthogonality Benefits from the (many) excess antennas Simplified multiuser processing Reduced transmit power Thermal noise and fast fading vanish Differences with MU MIMO in conventional cellular systems Time division duplexing used to enable channel estimation Pilot contamination limits performance F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, Scaling up MIMO: Opportunities and challenges with very large arrays, IEEE Signal Processing Mag., vol. 30, no. 1, pp , Jan

19 Centralized vs. Distributed Centralized 7 cells without sectorization 12 users uniformly distributed in each cell ISD = 500m BS Distributed BS antenna clusters Fixed number of base station antennas per cell * K. T. Truong and R. W. Heath, Jr., Impact of Spatial Correlation and Distributed Antennas for Massive MIMO systems, to appear in the Proceedings of the Asilomar Conference on Signals, Systems, and Computers, Nov. 3-6,

20 Potential Gains from Massive MIMO Uplink number of Downlink number of Number of base station antennas Number of base station antennas 7 cells without sectorization, 12 users uniformly distributed in each cell, ISD = 500m Distributing antennas achieves higher gains Saturation is not observed without huge # of antennas 20

21 Critical Issues in Massive MIMO Gains are not that big with not-so-many antennas Require many antennas to remove interference Need more coordination to remove effects of pilot contamination Massive MIMO seems to be more uplink driven Certain important roles are reserved between base stations and users A different layout of control and data channels may be required Practical effects are not well investigated Channel aging affects energy-focusing ability of narrow beams Spatial correlation reduces effective DoFs as increasing number of antennas Role of asynchronism in pilot contamination and resulting performance 21

22 Massive MIMO Conclusions Observations Pilot contamination is a big deal, but possibly overcome by coordination Performance is sensitive to channel aging effects * Good performance can be achieved with distributed antennas * Not clear how to pack so many microwave antennas on a base station Needs more extensive simulation study with realistic system parameters Forecast Massive MIMO will probably not be used in isolation Will be combined with distributed antennas or base station coordination Reduces the effects of pilot contamination Work with smaller numbers of antennas * K. T. Truong and R. W. Heath, Jr., Effects of Channel Aging in Massive MIMO Systems, to appear in the Journal of Communications and Networks, Special Issue on Massive MIMO, February

23 Outline MIMO in cellular networks Coordinated Multipoint a.k.a. network MIMO Massive MIMO Millimeter wave MIMO Comparison between technologies Parting thoughts 23

24 Why mmwave for Cellular? 1G-4G cellular 5G cellular Microwave m i l l i m e t e r w a v e 300 MHz 3 GHz 28 GHz GHz GHz 300 GHz Huge amount of spectrum available in mmwave bands* Cellular systems live with limited microwave spectrum ~ 600MHz 29GHz possibly available in 23GHz, LMDS, 38, 40, 46, 47, 49, and E-band Technology advances make mmwave possible Silicon-based technology enables low-cost highly-packed mmwave RFIC** Commercial products already available (or soon) for PAN and LAN Already deployed for backhaul in commercial products * Z. Pi,, and F. Khan. "An introduction to millimeter-wave mobile broadband systems." IEEE Communications Magazine, vol. 49, no. 6, pp , Jun ** T.S. Rappaport, J. N. Murdock, and F. Gutierrez. "State of the art in 60-GHz integrated circuits and systems for wireless communications." Proceedings of the IEEE, vol. 99, no. 8, pp: ,

25 The Need for Gain mmwave aperture mmwave noise bandwidth TX 2 4 microwave noise bandwidth RX microwave aperture Smaller wavelength means smaller captured energy at antenna 3GHz->30GHz gives 20dB extra path loss due to aperture Larger bandwidth means higher noise power and lower SNR 50MHz -> 500MHz bandwidth gives 10dB extra noise power Solution: Exploit array gain from large antenna arrays 25

26 Antenna Arrays are Important highly directional MIMO transmission Baseband Processing Baseband Processing antennas are small (mm) ~100 antennas Narrow beams are a new feature of mmwave Reduces fading, multi-path, and interference Implemented in analog due to hardware constraints Arrays will change system design principles used at TX and RX 26

27 Coverage Gains from Large Arrays LOS & blocked path loss model from measurements. A Poisson layout of mmwave BSs. Average cell radius Rc=100m. A Boolean scheme building model. Serving BS Typical User Buildings Coverage Probability Gain from directional antenna array 0.4 PPP Interfering BSs mmwave networks can provide acceptable coverage Directional array gain compensates severe path loss Smaller beamwidth reduces the effect of interference 0.3 Omni Directional Directional: HPBW=40 o Directional: HPBW=10 o SINR Threshold in db 27

28 Critical Issues in mmwave MIMO Dealing with hardware constraints Need a combination of analog and digital beamforming Array geometry may be unknown, may change Performance in complex propagation environments Evaluate performance with line-of-sight and blocked signal paths Must adapt to frequent blockages and support mobility Entire system must support directionality Need approval to employ the spectrum 28

29 mmwave Conclusions Observations Coverage may be acceptable with the right system configuration Strong candidate for higher per-link data rates Hardware can leverage insights from 60GHz LAN and PAN Highly directional antennas may radically change system design Supporting mobility may be a challenge Forecast Will be part of 5G if access to new spectrum becomes viable Most likely will co-exist with microwave cellular systems Will remain useful for niche applications like backhaul 29

30 Outline MIMO in cellular networks Coordinated Multipoint a.k.a. network MIMO Massive MIMO Millimeter wave MIMO Comparison between technologies Parting thoughts 30

31 Stochastic Geometry for Cellular performance analyzed for a typical user base station locations distributed (usually) as a Poisson point process (PPP) Baccelli Stochastic geometry is a tool for analyzing microwave cellular Reasonable fit with real deployments Closed form solutions for coverage probability available Provides a system-wide performance characterization Need to incorporate features of each technology J. G. Andrews, F. Baccelli, and R. K. Ganti, "A Tractable Approach to Coverage and Rate in Cellular Networks", IEEE Transactions on Communications, November 2011.! T. X. Brown, "Cellular performance bounds via shotgun cellular systems," IEEE JSAC, vol.18, no.11, pp.2443,2455, Nov

32 Comparing Different Approaches CoMP model Sectored cooperation model Typical user can be edge or center user of the cluster Several assumptions made to permit calculation mmwave model Directional antennas are incorporated as marks of the base station PPP Blockages due to buildings incorporated via random shape theory Massive MIMO model Analyze asymptotic case with infinite number of antennas at the base station No spatial correlation, includes estimation error, pilot contamination New expressions derived for each case Tianyang Bai and R. W. Heath, Jr., `` Asymptotic Coverage Probability and Rate in Massive MIMO Networks,'' submitted to IEEE Wireless Communications Letters, May Available on ArXiv.! Tianyag Bai and R. W. Heath Jr., ``Performance Analysis of mmwave Cellular Systems, under preparation. 32

33 SINR Coverage Comparison Gain from directional antennas and blockages in mmwave 0.8 SINR Coverage Probability CoMP: N t =4, 2 Users, 3 BSs/ Cluster Massive MIMO: N t = mmwave: N t =64, R c =100m SU MIMO: 4X4 Gain from larger number of antennas SINR Threhold in db SINR CCDF 33

34 Coverage Comparison Gain from directional antennas and blockages in mmwave Rate Coverage Probability CoMP: N t =4, 2 Users, 3 BSs/ Cluster Massive MIMO: N t = mmwave: N t =64, R c =100m SU MIMO: 4X4 Gain from massive number of antennas Spectrum Efficiency (bps/hz) Spectrum Efficiency CCDF 34

35 Rate Comparison Gain from larger bandwidth Rate Coverage Probability CoMP: N t =4, 2 Users, 3 BSs/ Cluster Massive MIMO: N t = mmwave: N t =64, R c =100m SU MIMO: 4X4 Gain from serving multiple users Cell Throughput in Mbps Rate CCDF 35

36 Rate Comparison Massive MIMO MMwave CoMP SU MIMO Signal BW 50 MHz 500 MHz 50 MHz 50 MHz User/ Cell bps/hz per user 5.47 bps/hz 8.00 bps/hz 4.34 bps/hz 4.95 bps/hz Rate/Cell bps/hz 8.00 bps/hz 8.68 bps/hz 4.95 bps/hz Capacity/ Cell 1.10 Gbps 4.00 Gbps 434 Mbps 248 Mbps mmwave outperforms due to more available BW * of course various parameters can be further optimized ** SU MIMO is 4x4 w/ zero-forcing receiver 36

37 Outline MIMO in cellular networks Coordinated Multipoint a.k.a. network MIMO Massive MIMO Millimeter wave MIMO Comparison between technologies Parting thoughts 37

38 Parting Thoughts cooperative MIMO massive MIMO mmwave MIMO Conclusions cooperation will be used in some form, more powerful with better infrastructure, need to be mindful of overheads in system design some potential for system rates, need large base station arrays, can be used with cooperation large potential for peak rates, more hardware challenges, requires more spectrum, more radical system design potential 38

39 Questions? Robert W. Heath Jr. The University of Texas at Austin