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
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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
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
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