mmWave Massive MIMO

A Paradigm for 5G

Editors

Shahid Mumtaz Institute de Telecomunicagöes, Aveiro, Portugal

Jonathan Rodriguez Institute de Telecomunicagöes, Aveiro, Portugal

Linglong Dai Tsinghua University, Beijing, China

ELSEVIER

AMSTERDAM • BOSTON • HEIDELBERG • LONDON

NEW YORK • OXFORD • PARIS • SAN DIEGO

SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Academic Press is an imprint of Elsevier

Contents

Contributors xiii Preface xv Acknowledgments xvii About the Editors xix

CHAPTER 1 Introduction to mmWave Massive MIMO 1 S. Mumtaz, J. Rodriguez and L. Dai

1.1 Requirements of Key Capabilities for 5G 2 1.2 5G Network Architecture Based on mmWave Massive MIMO 4 1.3 Challenges for mmWave Massive MIMO 7 1.4 Structure and Contributions of This Book 12

References 16

CHAPTER 2 SISO to mmWave Massive MIMO 19 D. Zhang, S. Mumtaz and K.S. Huq

2.1 Overview of Wireless Communication Evolution 19 2.2 The Channel Models Behind SISO, MIMO 20

2.2.1 Wireless Propagation Loss 20 2.2.2 Free Space Propagation Model 21 2.2.3 Ray Tracing 23 2.2.4 Empirical Models 25 2.2.5 Shadowing Effects 26

2.3 From SISO to MIMO 27 2.3.1 Outage Probability and Cell Coverage Area 27 2.3.2 Rayleigh and Rician Channel Models 28 2.3.3 Capacity and Transmission Rate Analysis 29

2.4 From MIMO to mMIMO 32 2.4.1 Even Faster Transmission Speed 33 2.4.2 Energy Efficiency 34

2.5 Emerging Topics in mmWave mMIMO 35 References 36

CHAPTER 3 Hybrid Antenna Array for mmWave Massive MIMO 39 J.A. Zhang, X. Huang, V. Dyadyuk and Y. Jay Guo

3.1 Introduction 39 3.2 Massive Hybrid Array Architectures 41 3.3 Hardware Design for Analog Subarray 43

3.3.1 Antenna Arrays 43

V

vi Contents

3.3.2 RF Chain Architectures 46 3.3.3 Hybrid Array Prototypes 49

3.4 Smart Antenna Techniques 51 3.4.1 Array Geometry 51 3.4.2 Pure Beamforming and AoA Estimation 52 3.4.3 Single-User MIMO 55 3.4.4 SDMA 57

3.5 Conclusions 59 References 60

CHAPTER 4 Encoding and Detection in mmWave Massive MIMO 63 S.A.R. Naqvi, S.A. Hassan and Z. Mulk

4.1 Introduction 63 4.2 Background 64 4.3 System Model 65 4.4 Multicell Uplink Communication 67

4.4.1 Uplink Training With Pilot Reuse 67 4.4.2 Actual Transmission of Data 68 4.4.3 Achievable Cell Throughput 69

4.5 Results 72 4.6 Conclusion 77

References 77

CHAPTER 5 Precoding for mmWave Massive MIMO 79 X. Gao, L Dai, Z. Gao, T. Xie and Z. Wang

5.1 Introduction 79 5.2 Channel Model for mmWave Massive MIMO 80 5.3 Digital Precoding 81

5.3.1 Single-User Digital Precoding 81 5.3.2 Multiuser Digital Precoding 83 5.3.3 Summary of Digital Precoding 84

5.4 Analog Beamforming 84 5.4.1 Beam Steering 85 5.4.2 Beam Training 86 5.4.3 Summary of Analog Beamforming 87

5.5 Hybrid Precoding 88 5.5.1 Single-User Hybrid Precoding 88 5.5.2 Multiuser Hybrid Precoding 103

5.6 Conclusions 108 References 109

Contents vii

CHAPTER 6 Channel Estimation for mmWave Massive MIMO Systems 113 Z. Gao, L. Dai, C. Hu, X. Gao and Z. Wang

6.1 Introduction 114 6.2 Preparatory Work 115

6.2.1 Channel Model 116 6.2.2 Transceiver Structure in mm Wave Massive MIMO 117

6.3 Compressive Sensing (CS)-Based Channel Estimation Schemes 119 6.3.1 Concept of CS Theory 119 6.3.2 Formulate Channel Estimation as CS Problem 120 6.3.3 Sparse Channels Reconstruction Via CS 122 6.3.4 Design Training Beam and Combining Patterns

According to CS Theory 123 6.3.5 Remark 125

6.4 Channel Estimation With One-Bit ADCs at the Receiver 125 6.4.1 Virtual Channel Representation of mm Wave Massive

MIMO Channels 126 6.4.2 The Maximal Likelihood (ML) Estimator 127 6.4.3 Estimate Channels With Iterative Approach 128 6.4.4 Remark 128

6.5 Parametric Channel Estimation Schemes for mm Wave Massive MIMO Systems 129 6.5.1 Super-Resolution Sparse Channel Estimation 129 6.5.2 Multiuser and Multistream (MU-MS) Hybrid

Beamforming/Combining 130 6.5.3 Numerical Simulations 132 6.5.4 Remark 134

6.6 Subspace Estimation and Decomposition (SED)-Based Channel Estimation 134 6.6.1 Subspace Estimation in Traditional MIMO

Systems 134 6.6.2 Extend to Hybrid MIMO Transceiver Structure 135 6.6.3 Remark 136

6.7 Other Channel Estimation Schemes 137 6.7.1 Can Channel Estimation Schemes in Massive MIMO

Be Tailored to mmWave Massive MIMO? 137 6.7.2 Codebook-Based Channel Estimation Schemes 137

6.8 Summary 138 References 138

viii Contents

CHAPTER 7 Channel Feedback for mmWave Massive MIMO 141 P.-H. Kuo, B. Su and C.-P. Yen

7.1 Introduction 141 7.2 Channel Feedback With Compressive Sensing 143

7.2.1 Algorithmic Framework of Compressive Sensing 144

7.2.2 Applications of Compressive Sensing to CSI Feedback Schemes 145

7.2.3 Sparsifying Basis 146 7.2.4 CSI Feedback Protocol With Adaptive Sparsifying

Basis 148 7.3 CSI Acquisition With Angular-Domain Beamforming 151

7.3.1 Multistage Beamforming and Feedback 151 7.4 Downlink Precoding in FDD Based on Angle of Arrival 154

7.4.1 Background and Assumptions 156 7.4.2 Downlink Precoding Design Using

Beamforming-Based Partial CSI 160 7.4.3 Simulation Results 162

7.5 Summary 163 References 166

CHAPTER 8 mmWave Massive MIMO Channel Modeling 169 B. Ai, K. Guan, G. Li and S. Mumtaz

8.1 Introduction 169 8.2 Specific Characteristics of mmWave Massive MIMO

Channels 172 8.2.1 Propagation Mechanisms of mmWave and

Sub-mmWave 172 8.2.2 mmWave Massive MIMO Static Channel 173 8.2.3 mmWave Massive MIMO Dynamic Channels 180

8.3 State-of-the-Art of Millimeter-Wave Massive MIMO Channel Study 180 8.3.1 Millimeter-Wave Massive MIMO Channel

Modeling 180 8.3.2 Millimeter-Wave Massive MIMO Channel

Sounding 190 8.4 Conclusion ; 192

References 193

Contents ix

CHAPTER 9 mmWave Communication Enabling Techniques for 5G Wireless Systems: A Link Level Perspective 195 T.E. Bogale, X. Wang and L.B. Le

9.1 Introduction 195 9.2 Beamforming 199

9.2.1 Digital Beamforming 200 9.2.2 Hybrid Beamforming 201 9.2.3 Link Level Performance 205

9.3 Spatial Multiplexing 209 9.4 Channel Estimation 211 9.5 Waveform Design 217 9.6 Access Strategy 219 9.7 Conclusions 220

References 220

CHAPTER 10 MAC Layer Design for mmWave Massive MIMO 227 G. Lee and Y. Sung

10.1 Introduction 227 10.2 Basic Scheduling Algorithms 230 10.3 User Scheduling in MU-MIMO 232

10.3.1 RBF and SUS 234 10.3.2 Limited Feedback, Quantization, and Two-Phase

User Scheduling 236 10.4 User Scheduling in Massive MIMO 238 10.5 User Scheduling in mmWave Massive MIMO 241

10.5.1 Randomly Directional Beamforming Under the UR-SP Channel Model 242

10.5.2 Extension to a General Sparse mmWave Channel Model 248

10.5.3 Efficient Scheduling Methods for Sparse mmWave MIMO Channels 249

10.6 Conclusions 252 References 253

CHAPTER 11 Enhanced Multiple-Access for mmWave Massive MIMO 257 M. Nasiri Khormuji

11.1 Introduction 258 11.1.1 Background on mMIMO 258 11.1.2 Scope and Contributions of the Chapter 258 11.1.3 Organization of the Chapter 259

11.2 Uplink Capacity Shortage of mMIMO 259 11.3 SOMA: Novel mMIMO Uplink 262

11.3.1 Transmitter 262 11.3.2 Receiver 263

11.4 Sum-Rate Characterization of SOMA 267 11.4.1 Bounds on SOMA 267 11.4.2 Bounds on Conventional TDD 268

11.5 Numerical Evaluations of SOMA 270 11.6 Generalized SOMA 274 11.7 Two-Group GSOMA 275

11.7.1 Mode 1: Without Blanking 276 11.7.2 Mode 2: With Blanking 278

11.8 Numerical Evaluations of GSOMA 279 11.9 Conclusions 281

Appendix 281 ll .A Proof of Proposition 11.1 281 ll .B Proof of Proposition 11.2 283 ll .C Proof of Proposition 11.3 284 ll .D Proof of Proposition 11.5 285 ll .E Proof of Proposition 11.6 286 References 287

CHAPTER 12 Fronthaul Design for mmWave Massive MIMO 289 Z. Gao, L. Dai, X. Gao, M.Z. Shakir and Z. Wang

12.1 Introduction 290 12.2 A Survey of Existing Fronthaul Solutions 292

12.2.1 Category of Fronthaul Solutions 292 12.2.2 Fronthaul Network Topology 294 12.2.3 Pros and Cons of Different Spectrum Resources for

Fronthaul 295 12.2.4 Why and How Will We Exploit mmWave for

Fronthaul? 296 12.3 Market Requirements of mmWave Fronthaul 299

12.3.1 Total Cost of Ownership (TCO) 299 12.3.2 Throughput Requirement of Fronthaul Network 300 12.3.3 Traffic Classes and Latency 301 12.3.4 Intercell/Intersite Distance (ISD) 302

12.4 mmWave Massive MIMO-Based Fronthaul Solution 303 12.4.1 Concept of mmWave Massive MIMO-Based

Fronthaul 303

Contents xi

12.4.2 Antenna Techniques 304 12.4.3 Reliable Channel Estimation Schemes 306 12.4.4 Flexible Beamforming Design 308 12.4.5 Time Division Duplex (TDD), Frequency

Division Duplex (FDD), or Full Duplex? 309 12.4.6 In-Band, Out-Band, or Hybrid-Band 310

12.5 Summary 311 References 311

CHAPTER 13 mmWave Cellular Networks: Stochastic Geometry Modeling, Analysis, and Experimental Validation 313 W. Lu and M. Di Renzo

13.1 Introduction 314 13.2 System Model 315

13.2.1 PPP-Based Abstraction Modeling 315 13.2.2 Directional Beamforming Modeling 316 13.2.3 Link State Modeling 317 13.2.4 Path-Loss Modeling 317 13.2.5 Shadowing Modeling 318 13.2.6 Cell Association Criterion 318 13.2.7 Problem Formulation 319

13.3 Preliminaries: Analysis and Approximations of Transformations of the Path-Loss 320 13.3.1 Two-Ball Approximation 321 13.3.2 Communication Blockage Probability 324

13.4 Modeling Coverage and Rate: Noise-Limited Approximation 325

13.5 Modeling Coverage and Rate: Accurate Modeling of the Other-Cell Interference 326

13.6 Numerical and Simulation Results 329 13.6.1 Experimental Validation of PPP-Based Modeling 329 13.6.2 Validation of the Noise-Limited Approximation 331

13.7 Conclusion 336 Appendix 336 13.A Proofs of the Results in Section 13.3 336 13.B Proofs of the Results in Section 13.4 337 13.C Proofs of the Results in Section 13.5 338 References : 339

Index 343