On the Complementary Benefits of Massive MIMO, Small Cells, and TDD

Similar documents
Massive MIMO and HetNets: Benefits and Challenges

Analysis of massive MIMO networks using stochastic geometry

System Performance of Cooperative Massive MIMO Downlink 5G Cellular Systems

Interference Management in Two Tier Heterogeneous Network

Analysis and Improvements of Linear Multi-user user MIMO Precoding Techniques

ON PILOT CONTAMINATION IN MASSIVE MULTIPLE-INPUT MULTIPLE- OUTPUT SYSTEM WITH LEAST SQUARE METHOD AND ZERO FORCING RECEIVER

Making Smart Use of Excess Antennas: Massive MIMO, Small Cells, and TDD

Bringing the Magic of Asymptotic Analysis to Wireless Networks

Massive MIMO a overview. Chandrasekaran CEWiT

Precoding and Massive MIMO

Decentralized Resource Allocation and Effective CSI Signaling in Dense TDD Networks

Hype, Myths, Fundamental Limits and New Directions in Wireless Systems

Coordinated Multi-Point Transmission for Interference Mitigation in Cellular Distributed Antenna Systems

Canadian Evaluation Group

S. Mohammad Razavizadeh. Mobile Broadband Network Research Group (MBNRG) Iran University of Science and Technology (IUST)

MIMO and Beamforming in the 5G Context SBrT 2017

Designing Energy Efficient 5G Networks: When Massive Meets Small

Millimeter-Wave Communication and Mobile Relaying in 5G Cellular Networks

Pilot-Decontamination in Massive MIMO Systems via Network Pilot Data Alignment

LTE-Advanced research in 3GPP

WINNER+ IMT-Advanced Evaluation Group

Field Test of Uplink CoMP Joint Processing with C-RAN Testbed

Long Term Evolution (LTE) and 5th Generation Mobile Networks (5G) CS-539 Mobile Networks and Computing

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

Massive MIMO Full-duplex: Theory and Experiments

Beamforming for 4.9G/5G Networks

Distributed Multi- Cell Downlink Transmission based on Local CSI

Providing Extreme Mobile Broadband Using Higher Frequency Bands, Beamforming, and Carrier Aggregation

Addressing Future Wireless Demand

Technical Aspects of LTE Part I: OFDM

Optimizing Multi-Cell Massive MIMO for Spectral Efficiency

Coordinated Multi-Point (CoMP) Transmission in Downlink Multi-cell NOMA Systems: Models and Spectral Efficiency Performance

UL/DL Mode Selection and Transceiver Design for Dynamic TDD Systems

Deployment and Radio Resource Reuse in IEEE j Multi-hop Relay Network in Manhattan-like Environment

5G System Concept Seminar. RF towards 5G. Researchers: Tommi Tuovinen, Nuutti Tervo & Aarno Pärssinen

Performance Analysis of Power Control and Cell Association in Heterogeneous Cellular Networks

A Practical Channel Estimation Scheme for Indoor 60GHz Massive MIMO System. Arumugam Nallanathan King s College London

Scheduling Algorithm for Coordinated Beamforming in Heterogeneous Macro / Pico LTE-Advanced Networks

IEEE Working Group on Mobile Broadband Wireless Access <

Interference management Within 3GPP LTE advanced

5G: Opportunities and Challenges Kate C.-J. Lin Academia Sinica

EasyChair Preprint. A User-Centric Cluster Resource Allocation Scheme for Ultra-Dense Network

mm Wave Communications J Klutto Milleth CEWiT

Interference Mitigation Using Uplink Power Control for Two-Tier Femtocell Networks

LTE-Advanced and Release 10

MIMO Systems and Applications

MU-MIMO in LTE/LTE-A Performance Analysis. Rizwan GHAFFAR, Biljana BADIC

Emerging Technologies for High-Speed Mobile Communication

Channel Estimation and Multiple Access in Massive MIMO Systems. Junjie Ma, Chongbin Xu and Li Ping City University of Hong Kong, Hong Kong

Uplink and Downlink Rate Analysis of a Full-Duplex C-RAN with Radio Remote Head Association

Dynamic Frequency Hopping in Cellular Fixed Relay Networks

Analytical Evaluation of the Cell Spectral Efficiency of a Beamforming Enhanced IEEE m System

Spectrum Efficiency for Future Wireless Communications

Performance Studies on LTE Advanced in the Easy-C Project Andreas Weber, Alcatel Lucent Bell Labs

An Overview of Massive MIMO Technology Components in METIS

Lecture LTE (4G) -Technologies used in 4G and 5G. Spread Spectrum Communications

Non-Orthogonal Multiple Access (NOMA) in 5G Cellular Downlink and Uplink: Achievements and Challenges

Massive MIMO for 5G below 6 GHz Achieving Spectral Efficiency, Link Reliability, and Low-Power Operation

Distributed Coordinated Multi-Point Downlink Transmission with Over-the-Air Communication

Energy Efficient Power Control for the Two-tier Networks with Small Cells and Massive MIMO

Inter-Cell Interference Mitigation in Cellular Networks Applying Grids of Beams

5G: New Air Interface and Radio Access Virtualization. HUAWEI WHITE PAPER April 2015

3G Evolution HSPA and LTE for Mobile Broadband Part II

Novel Transmission Schemes for Multicell Downlink MC/DS-CDMA Systems Employing Time- and Frequency-Domain Spreading

A 5G Paradigm Based on Two-Tier Physical Network Architecture

MATLAB COMMUNICATION TITLES

Massive MIMO: Ten Myths and One Critical Question. Dr. Emil Björnson. Department of Electrical Engineering Linköping University, Sweden

Pilot Contamination Reduction Scheme in Massive MIMO Multi-cell TDD Systems

Radio Interface and Radio Access Techniques for LTE-Advanced

Next Generation Mobile Communication. Michael Liao

Beyond 4G: Millimeter Wave Picocellular Wireless Networks

Beyond 4G Cellular Networks: Is Density All We Need?

An Advanced Wireless System with MIMO Spatial Scheduling

5G deployment below 6 GHz

2-2 Advanced Wireless Packet Cellular System using Multi User OFDM- SDMA/Inter-BTS Cooperation with 1.3 Gbit/s Downlink Capacity

Coherent Joint-Processing CoMP in Pico-Cellular Lamp-Post Street Deployment

Experimental evaluation of massive MIMO at 20 GHz band in indoor environment

742 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, VOL. 8, NO. 5, OCTOBER An Overview of Massive MIMO: Benefits and Challenges

All rights reserved. Mobile Developments. Presented by Philippe Reininger, Chairman of 3GPP RAN WG3

Analysis of Massive MIMO With Hardware Impairments and Different Channel Models

Hybrid Transceivers for Massive MIMO - Some Recent Results

Pilot Contamination: Is It Really A Stumbling Block For Massive MIMO?

Multiple Antenna Processing for WiMAX

On the Value of Coherent and Coordinated Multi-point Transmission

Performance Analysis of Massive MIMO Downlink System with Imperfect Channel State Information

Novel Detection Scheme for LSAS Multi User Scenario with LTE-A and MMB Channels

MU-MIMO with Fixed Beamforming for

Optimized Data Symbol Allocation in Multicell MIMO Channels

NR Physical Layer Design: NR MIMO

Planning of LTE Radio Networks in WinProp

Interference Mitigation by MIMO Cooperation and Coordination - Theory and Implementation Challenges

Multiple Antennas. Mats Bengtsson, Björn Ottersten. Basic Transmission Schemes 1 September 8, Presentation Outline

Interference Alignment for Heterogeneous Full-Duplex Cellular Networks. Amr El-Keyi and Halim Yanikomeroglu

Adaptive Modulation and Coding for LTE Wireless Communication

On Practical Coexistence Gaps in. A. Zubow, P. Gawłowicz, S. Bayhan European Wireless 2018

Uplink Sounding Reference Signal Coordination to Combat Pilot Contamination in 5G Massive MIMO

Potential Throughput Improvement of FD MIMO in Practical Systems

(some) Device Localization, Mobility Management and 5G RAN Perspectives

Analysis of Novel Eigen Beam Forming Scheme with Power Allocation in LSAS

How to tackle 5G challenges Dr. Dominique Noguet Head of Communication and Security Technologies Dpt CEA-LETI

Transcription:

On the Complementary Benefits of Massive MIMO, Small Cells, and TDD Jakob Hoydis (joint work with K. Hosseini, S. ten Brink, M. Debbah) Bell Laboratories, Alcatel-Lucent, Germany Alcatel-Lucent Chair on Flexible Radio, Supélec, France jakob.hoydis@alcatel-lucent.com IEEE Communication Theory Workshop Phuket, Thailand June 23-26, 2013 Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 1 / 23

The data explosion and possible solutions By 2017, there will be 13 more mobile data traffic than in 2012. 1 Network densification is the only solution to the capacity crunch: Small cells : + area spectral efficiency scales linearly with the cell density not well suited to provide coverage and support high mobility Massive MIMO : + interference can be almost entirely eliminated distributing the antennas achieves highest capacity 2 1 Source: Cisco, Yankee 2 H. S. Dhillon, M. Kountouris, and J. G. Andrews, Downlink MIMO hetnets: Modeling, ordering results and performance analysis, IEEE Trans. Wireless Commun., 2013, submitted. [Online]. Available: http://arxiv.org/abs/1301.5034. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 2 / 23

The data explosion and possible solutions By 2017, there will be 13 more mobile data traffic than in 2012. 1 Network densification is the only solution to the capacity crunch: Small cells : + area spectral efficiency scales linearly with the cell density not well suited to provide coverage and support high mobility Massive MIMO : + interference can be almost entirely eliminated distributing the antennas achieves highest capacity 2 Both approaches can significantly reduce the radiated power Mobility is not anymore limited by coverage but rather by battery life. 1 Source: Cisco, Yankee 2 H. S. Dhillon, M. Kountouris, and J. G. Andrews, Downlink MIMO hetnets: Modeling, ordering results and performance analysis, IEEE Trans. Wireless Commun., 2013, submitted. [Online]. Available: http://arxiv.org/abs/1301.5034. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 2 / 23

The data explosion and possible solutions By 2017, there will be 13 more mobile data traffic than in 2012. 1 Network densification is the only solution to the capacity crunch: Small cells : + area spectral efficiency scales linearly with the cell density not well suited to provide coverage and support high mobility Massive MIMO : + interference can be almost entirely eliminated distributing the antennas achieves highest capacity 2 Both approaches can significantly reduce the radiated power Mobility is not anymore limited by coverage but rather by battery life. Can we integrate the complementary benefits of both in a new network architecture? 1 Source: Cisco, Yankee 2 H. S. Dhillon, M. Kountouris, and J. G. Andrews, Downlink MIMO hetnets: Modeling, ordering results and performance analysis, IEEE Trans. Wireless Commun., 2013, submitted. [Online]. Available: http://arxiv.org/abs/1301.5034. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 2 / 23

A two-tier network architecture Massive MIMO base stations (BS) overlaid with many small cells (SCs) BSs ensure coverage and serve highly mobile UEs SCs drive the capacity (hot spots, indoor coverage) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 3 / 23

A two-tier network architecture Massive MIMO base stations (BS) overlaid with many small cells (SCs) BSs ensure coverage and serve highly mobile UEs SCs drive the capacity (hot spots, indoor coverage) Intra- and inter-tier interference is the main performance bottleneck. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 3 / 23

A two-tier network architecture Massive MIMO base stations (BS) overlaid with many small cells (SCs) BSs ensure coverage and serve highly mobile UEs SCs drive the capacity (hot spots, indoor coverage) Intra- and inter-tier interference is the main performance bottleneck. There are many excess antennas in the network which should be exploited! Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 3 / 23

The essential role of TDD A network-wide synchronized TDD protocol and the resulting channel reciprocity have the following advantages: The downlink channels can be estimated from uplink pilots. Necessary for massive MIMO Channel reciprocity holds for the desired and the interfering channels. Knowledge about the interfering channels can be acquired for free. TDD enables the use of excess antennas to reduce intra-/inter-tier interference. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 4 / 23

An idea from cognitive radio 1 The secondary BS listens to the transmission from the primary UE: y = hx + n 2...and computes the covariance matrix of the received signal: [ E yy H] = hh H + SNR 1 I Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 5 / 23

An idea from cognitive radio 3 With the knowledge of the SNR, the secondary BS designs a precoder w which is orthogonal to the sub-space spanned by hh H. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 6 / 23

An idea from cognitive radio 3 With the knowledge of the SNR, the secondary BS designs a precoder w which is orthogonal to the sub-space spanned by hh H. 4 The interference to the primary UE can be entirely eliminated without explicit knowledge of h. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 6 / 23

Translating this idea to HetNets Every device estimates its received interference covariance matrix and precodes (partially) orthogonally to the dominating interference subspace. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 7 / 23

Translating this idea to HetNets Every device estimates its received interference covariance matrix and precodes (partially) orthogonally to the dominating interference subspace. Advantages Reduces interference towards the directions from which most interference is received. No feedback or data exchange between the devices is needed. Every device relies only on locally available information. The scheme is fully distributed and, thus, scalable. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 7 / 23

About the literature Cognitive radio R. Zhang, F. Gao, and Y. C. Liang, Cognitive Beamforming Made Practical: Effective Interference Channel and Learning-Throughput Tradeoff, IEEE Trans. Commun., 2010. F. Gao, R. Zhang, Y.-C. Liang, X. Wang, Design of Learning-Based MIMO Cognitive Radio Systems, IEEE Trans. Veh. Tech., 2010. H. Yi, Nullspace-Based Secondary Joint Transceiver Scheme for Cognitive Radio MIMO Networks Using Second-Order Statistics, ICC, 2010. TDD Cellular systems S. Lei and S. Roy, Downlink multicell MIMO-OFDM: an architecture for next generation wireless networks, WCNC, 2005. B. O. Lee, H. W. Je, I. Sohn, O. S. Shin, and K. B. Lee, Interference-aware Decentralized Precoding for Multicell MIMO TDD Systems, Globecom. 2008. Blind nullspace learning Y. Noam and A. J. Goldsmith, Exploiting spatial degrees of freedom in MIMO cognitive radio systems, ICC, 2012. and many more... Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 8 / 23

System model and signaling Each BS has N antennas and serves K single-antenna MUEs. S SCs per BS with F antennas serving 1 single-antenna SUE each The BSs and SCs have perfect CSI for the UEs they want to serve. Every device knows perfectly its interference covariance matrix and the noise power. Linear MMSE detection at all devices Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 9 / 23

System model and signaling Each BS has N antennas and serves K single-antenna MUEs. S SCs per BS with F antennas serving 1 single-antenna SUE each The BSs and SCs have perfect CSI for the UEs they want to serve. Every device knows perfectly its interference covariance matrix and the noise power. Linear MMSE detection at all devices The BSs and SCs use precoding vectors of the structure: ( 1 w PHH H + κq + σ I) 2 h h channel vector to the targeted UE H channel matrix to other UEs in the same cell P, σ 2 : transmit and noise powers Q interference covariance matrix κ: regularization parameter (α for BSs, β for SCs) About the regularization parameters For α, β = 0, the BSs and SCs transmit as if they were in an isolated cell, i.e., MMSE precoding (BSs) and maximum-ratio transmissions (SCs). By increasing α, β, the precoding vectors become increasingly orthogonal to the interference subspace. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 9 / 23

Comparison of duplexing schemes and co-channel deployment FDD TDD frequency SC DL SC UL BS DL BS UL frequency SC UL BS UL SC DL BS DL time time co-channel TDD co-channel reverse TDD frequency SC UL SC DL frequency SC DL SC UL BS UL BS DL BS UL BS DL time time FDD: Channel reciprocity does not hold TDD: Only intra-tier interference can be reduced co-channel (reverse) TDD: Inter and intra-tier interference can be reduced Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 10 / 23

TDD versus reverse TDD (RTDD) Order of UL/DL periods decides which devices interfere with each other. The BS-SC channels change very slowly. Thus, the estimation of the covariance matrix becomes easier for RTDD. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 11 / 23

Numerical results BS 1000 m 40 m 111 m SC MUE SUE 3 3 grid of BSs with wrap around S = 81 SCs per cells on a regular grid K = 20 MUEs randomly distributed 1 SUE per SC randomly distributed on a disc around each SC 3GPP channel model with path loss, shadowing and fast fading, N/LOS links TX powers: 46 dbm (BS), 24 dbm (SC), 23 dbm (MUE/SUE) 20 MHz bandwidth @ 2 GHz No user scheduling, power control Averages over channel realizations and UE locations TDD UL/DL cycles of equal length Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 12 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 0 0 20 40 60 80 Macro DL area spectral efficiency ( b/s/hz/km 2) FDD (N = 20, F = 1) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 FDD region more antennas N = 20 100 FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) 0 0 20 40 60 80 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 FDD region more antennas N = 20 100 TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 0 0 20 40 60 80 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 FDD region more antennas N = 20 100 TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 0 0 20 40 60 80 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 FDD region more antennas N = 20 100 0 0 20 40 60 80 α TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 FDD region β more antennas N = 20 100 0 0 20 40 60 80 α TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 CoTDD region FDD region β more antennas N = 20 100 0 0 20 40 60 80 α TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 CoTDD region FDD region β more antennas N = 20 100 0 0 20 40 60 80 α TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 α CoTDD region FDD region β more antennas N = 20 100 0 0 20 40 60 80 α TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 β α CoTDD region FDD region β more antennas N = 20 100 0 0 20 40 60 80 α TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink spectral area efficiency regions SC DL area spectral efficiency ( b/s/hz/km 2) 400 300 200 100 F = 1 4 β = 0 1 β α CoTDD region FDD region CoRTDD region β more antennas N = 20 100 0 0 20 40 60 80 α TDD region FDD (N = 20, F = 1) FDD/TDD (N = 100, F = 4) TDD (N = 100, F = 4, α = 1, β = 1) less intra-tier interf. α = 0 1 Macro DL area spectral efficiency ( b/s/hz/km 2) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 13 / 23

Downlink SINR distribution 1 0.8 MUE TDD Downlink SINR: Pr (SINR x) 0.6 0.4 0.2 α β SUE MUE SUE α = 0 α = 1 β = 0 β = 1 Mean 13.11 24.13 23.9 33.78 95% 40.38 48.47 40 42.87 50% 11.58 22.01 24.65 34.35 5% 8.48 7.86 6.02 22.62 0 20 10 0 10 20 30 40 SINR (db) 1 0.8 MUE Co-channel TDD Downlink SINR: Pr (SINR x) 0.6 0.4 0.2 α,β α,β SUE MUE SUE α, β = 0 α, β = 1 α, β = 0 α, β = 1 Mean 6.29 9.52 14.33 25.45 95% 20.45 35.95 29.88 35.01 50% 8.06 6.44 15.49 26.05 5% 26.64 6.82 6.51 13.6 0 20 10 0 10 20 30 40 SINR (db) Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 14 / 23

Uplink spectral area efficiency regions Small cell UL sum-rate ( b/s/hz/km 2) 400 300 200 100 F = 1 4 α more antennas N = 20 100 FDD/TDD (N = 20, F = 1) 0 0 20 40 60 80 Macro UL sum-rate ( b/s/hz/km 2) FDD/TDD (N = 100, F = 4) co-channel TDD co-channel reverse TDD Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 15 / 23

Observations With the proposed precoding scheme, a TDD co-channel deployment of BSs and SCs leads to the highest area spectral efficiency (α = β = 1, 20 MHz BW): DL UL Area throughput 7.63 Gb/s/km 2 8.93 Gb/s/km 2 Rate per MUE 38.2 Mb/s 25.4 Mb/s Rate per SUE 84.8 Mb/s 104 Mb/s Even a few excess antennas at the SCs lead to significant gains. As the scheme is fully distributed and requires no data exchange between the devices, the rates can be simply increased by adding more antennas to the BSs/SCs or increasing the SC-density. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 16 / 23

Discussion Channel reciprocity requires: Hardware calibration Scheduling of UEs on the same resource blocks in subsequent UL/DL cycles The network-wide TDD protocol requires tight synchronization of all devices: GPS (outdoor) NTP/PTP (indoor) BS reference signals Channel estimation will suffer from interference and pilot contamination. Covariance matrix estimation becomes difficult for large N. We have considered a worst-case outdoor deployment scenario with fixed cell association, no power control or scheduling. Location-dependent user scheduling and interference-temperature power control could further enhance the performance. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 17 / 23

Massive MIMO for wireless backhaul small cell wireless backhaul Core network wired backhaul massive MIMO base station wireless data user equipment The unrestrained SC-deployment where needed rather than where possible requires a high-capacity and easily accessible backhaul network. Already for most WiFi deployments, the backhaul capacity (10 100 Mbit/s) and not the air interface (54 600 Mbit/s) is the bottleneck. Why not provide wireless backhaul with massive MIMO? 3 3 T. L. Marzetta and H. Yang, Dedicated LSAS for metro-cell wireless backhaul - Part I: Downlink, Bell Laboratories, Alcatel-Lucent, Tech. Rep., Dec. 2012. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 18 / 23

Massive MIMO for wireless backhaul: Advantages No standardization or backward-compatibility required BS-SC channels change very slowly over time: Complex transmission/detection schemes (e.g., CoMP) can be easily implemented. Even FDD might be possible due to reduced CSI overhead. Provide backhaul where needed: Adapt backhaul capacity to the load (support highly variable traffic) Statistical multiplexing opportunity to avoid over-provisioning of backhaul Enable user-centric small-cell clustering for virtual MIMO SCs require only a power connection to be operational Line-of-sight not necessary if operated at low frequencies Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 19 / 23

Massive MIMO for wireless backhaul: Is it feasible? How many antennas are needed to satisfy the desired backhaul rates with a given transmit power budget? Assumptions: Every BS knows the channels to all SCs. The BSs can exchange some control information. Full user data sharing between the BSs is not possible. Single-antenna SCs, BSs with N antennas TDD operation on a separate band (2/3 DL, 1/3 UL) Same modeling assumptions as before Find the smallest N such that the power minimization problem with target SINR constraints for the multi-cell multi-antenna wireless system is feasible. 4,5 4 H. Dahrouj and W. Yu, Coordinated beamforming for the multicell multi-antenna wireless system, IEEE Trans. Wireless Commun., vol. 9, no. 5, pp. 1748 1759, May 2010. 5 S. Lakshminarayana, J. Hoydis, M. Debbah, and M. Assaad, Asymptotic analysis of distributed multi-cell beamforming, in IEEE International Symposium in Personal Indoor and Mobile Radio Communications (PIMRC), Istanbul, Turkey, Sep. 2010, pp. 2105 2110. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 20 / 23

Massive MIMO for wireless backhaul: Numerical results Required # of BS-antennas 500 400 300 200 100 S = 81 S = 40 S = 20 0 0 20 40 60 80 100 Downlink backhaul rate (Mbit/s) 0 10 20 30 40 50 Uplink backhaul rate (Mbit/s) Average minimum number of required BS-antennas N to serve S {20, 40, 81} randomly chosen SCs with the same target backhaul rate. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 21 / 23

Summary Massive MIMO and SCs have distinct advantages which complement each other: Massive MIMO for coverage and mobility support SCs for capacity and indoor coverage TDD and the resulting channel reciprocity allow every device to fully exploit its available degrees of freedom for intra-/inter-tier interference mitigation. A TDD co-channel deployment of massive MIMO BSs and SCs can achieve a very attractive rate region. Massive MIMO BSs can provide wireless backhaul to a large number of SCs. The slowly time-varying nature of the BS-SC channels might allow for complex precoding and detection schemes. For more details: J. Hoydis, K. Hosseini, S. ten Brink, and M. Debbah, Making Smart Use of Excess Antennas: Massive MIMO, Small Cells, and TDD, Bell Labs Technical Journal, vol. 18, no. 2, Sep. 2013. Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 22 / 23

Thank you! Jakob Hoydis (Bell Labs) Massive MIMO, Small Cells, and TDD CTW 2013 23 / 23