Written in a clear and concise manner, this book presents readers with an in-depth discussion of the 5G technologies that will help move society beyond its current capabilities. It perfectly illustrates how the technology itself will benefit both individual consumers and industry as the world heads towards a more connected state of being. Every technological application presented is modeled in a schematic diagram and is considered in depth through mathematical analysis and performance assessment. Furthermore, published simulation data and measurements are checked.
Each chapter of 5G Physical Layer Technologies contains texts, mathematical analysis, and applications supported by figures, graphs, data tables, appendices, and a list of up to date references, along with an executive summary of the key issues. Topics covered include: the evolution of wireless communications; full duplex communications and full dimension MIMO technologies; network virtualization and wireless energy harvesting; Internet of Things and smart cities; and millimeter wave massive MIMO technology. Additional chapters look at millimeter wave propagation losses caused by atmospheric gases, rain, snow, building materials and vegetation; wireless channel modeling and array mutual coupling; massive array configurations and 3D channel modeling; massive MIMO channel estimation schemes and channel reciprocity; 3D beamforming technologies; and linear precoding strategies for multiuser massive MIMO systems. Other features include:
- In depth coverage of a hot topic soon to become the backbone of IoT connecting devices, machines, and vehicles
- Addresses the need for green communications for the 21st century
- Provides a comprehensive support for the advanced mathematics exploited in the book by including appendices and worked examples
- Contributions from the EU research programmes, the International telecommunications companies, and the International standards institutions (ITU; 3GPP; ETSI) are covered in depth
- Includes numerous tables and illustrations to aid the reader
- Fills the gap in the current literature where technologies are not explained in depth or omitted altogether
5G Physical Layer Technologies is an essential resource for undergraduate and postgraduate courses on wireless communications and technology. It is also an excellent source of information for design engineers, research and development engineers, the private-public research community, university research academics, undergraduate and postgraduate students, technical managers, service providers, and all professionals involved in the communications and technology industry.
Table of Contents
Preface xvii
Acknowledgements xix
List of Mathematical Notation xxi
List of Wireless Network Symbols xxiii
List of Abbreviations xxv
Structure of the Book xxix
1 Introduction 1
1.1 Motivations 1
1.2 Overview of Contemporary Cellular Wireless Networks 4
1.3 Evolution of Wireless Communications in 3GPP Releases 7
1.3.1 3GPP Release 8 7
1.3.2 3GPP Release 9 8
1.3.3 3GPP Release 10 8
1.3.4 3GPP Release 11 8
1.3.5 3GPP Release 12 9
1.3.6 3GPP Release 13 9
1.3.7 3GPP Release 14 9
1.3.8 3GPP Release 15 (5G phase 1) 10
1.3.9 3GPP Release 16 (5G phase 2) 10
1.4 Multiuser Wireless Network Capacity Regions 10
1.4.1 The Capacity Region for Multiuser Channel 12
1.4.2 Analysis of Degraded BC with Superposition Coding 12
1.4.3 The Capacity Region for Multiuser MIMO Channel 14
1.4.4 The MIMO MAC Capacity Region 14
1.4.5 The MIMO BC Capacity Region 17
1.5 Fading Wireless Channels 19
1.6 Multicell MIMO Channels 20
1.7 Green Wireless Communications for the Twenty-First Century 20
1.7.1 Network Power Consumption Model 22
1.7.2 Antenna Interface Losses 22
1.7.3 Power Amplifier (PA) 22
1.8 BS Power Model 25
1.8.1 Small-Signal RF Transceiver 25
1.8.2 Baseband (BB) Unit 25
1.8.3 Power Supply and Cooling 25
1.8.4 BS Power Consumption at Variable Load 26
1.9 Green Cellular Networks 28
1.10 Green Heterogeneous Networks 30
1.11 Summary 31
1.A Tutorials on Theory and Techniques of Optimization Mathematics: Basics 33
1.A.1 Optimization of Unconstrained Function with a Single Variable 33
1.A.2 Optimization of Unconstrained Function with Multiple Variables 34
1.A.3 The Hessian Matrix 35
1.B Theory of Optimization Mathematics 36
1.B.1 Constrained Optimization 37
1.B.2 Bordered Hessian Matrix HB 37
1.C Karush-Kuhn-Tucker (KKT) Conditions 39
References 41
2 5G Enabling Technologies: Small Cells, Full-Duplex Communications, and Full-Dimension MIMO Technologies 43
2.1 Introduction 43
2.2 The Rationale for 5G Enabling Technologies 45
2.3 Network Densification 46
2.4 Cloud-Based Radio Access Network (C-RAN) 49
2.4.1 Resource Management Between Macrocells and Small Cells 51
2.4.2 BBU-RRH Switching Schemes 53
2.4.3 Mobile Small Cells 54
2.4.4 Automatic Self-Organising Network (SON) 56
2.5 Cache-Enabled Small-Cell Networks (CE-SCNs) 57
2.5.1 File Delivery Performance Analysis of CE-SCN 58
2.5.2 Outage Probability and Average File Delivery Rate in CE-SC System 59
2.6 Full-Duplex (FD) Communications 61
2.6.1 Analysis of FD Communication 63
2.6.2 FD Transmission Between Two Nodes 64
2.6.3 Principles of Self-Interference 65
2.6.4 Theoretical Example Analysis of Antenna Cancellation 67
2.6.5 Infrastructure for FD Transmission 68
2.6.6 Full-Duplex MAC (FD-MAC) Protocol 71
2.7 Review of Reference Signals, Antenna Ports, and Channels 74
2.7.1 DL and UL Physical Channels 75
2.7.2 DL Reference Signals and Antenna Ports 75
2.7.3 UL Reference Signals 76
2.7.3.1 UL Reference Signal Sequence Generation 76
2.7.3.2 Demodulation Reference Signal for PUSCH 77
2.7.3.3 Demodulation Reference Signal for PUCCH 78
2.7.3.4 Sounding Reference Signal SRS 78
2.7.3.5 Random-Access Channel Preambles 78
2.8 Full-Dimension MIMO Technology 79
2.8.1 Full-Dimension MIMO (FD-MIMO) Analysis 81
2.8.2 FD-MIMO System Design Issues 82
2.8.3 3GPP Development of 3D Model for FD-MIMO System 82
2.8.3.1 Antenna Array Elements Radiation Patterns 82
2.8.3.2 Antenna Configurations 83
2.8.3.3 FD-MIMO Development 84
2.8.4 Beamformed CSI-RS Transmission 85
2.8.5 CSI Feedback for FD-MIMO Systems 86
2.9 Summary 88
2.A Notes on Machine Learning Algorithms 89
2.A.1 The Algorithm 89
2.B Outage Probability in CE-SC Networks 91
2.B.1.1 Analysis of Term i: 91
2.C Signal Power at the Receive Antenna after Antenna Cancellation of Self-Interference 94
References 95
Further Reading 98
3 5G Enabling Technologies: Network Virtualization and Wireless Energy Harvesting 99
3.1 Introduction 99
3.2 Network Sharing and Virtualization of Wireless Resources 100
3.2.1 Earlier Network Sharing 100
3.2.2 Functional Description of Network Sharing Nodes 102
3.2.2.1 User Equipment (UE) Functions 102
3.2.2.2 Radio Network Controller (RNC) Functions 103
3.2.2.3 Evolved Node B (eNB) Functions 103
3.2.2.4 Base Station Controller (BSC) Functions 103
3.2.2.5 Mobile Switching Centre (MSC) Functions 103
3.2.2.6 Mobility Management Entity (MME) Functions 104
3.2.3 Single BS Shared by a Set of Operators 104
3.3 Evolved Resource Sharing 107
3.3.1 Principle of Cellular Network Evolved Resource Sharing 109
3.3.2 Single-Level Resource Allocation Among Operators 109
3.3.3 Opportunistic Sharing-Based Resource Allocation 112
3.4 Network Functions Virtualization (NFV) 113
3.4.1 Virtualized Network Functions 116
3.4.2 Principles of the Network Functions Virtualization Infrastructure (NFVI) 116
3.5 vRAN Supporting Fronthaul 117
3.5.1 Splitting the Architecture 118
3.5.1.1 Downlink (DL) 118
3.5.1.2 Uplink (UL) 118
3.6 Virtual Evolved Packet Core (vEPC) 119
3.7 Virtualized Switches 121
3.8 Auction in Resource Provision 121
3.9 Hierarchical Combinatorial Auction Models 122
3.10 Energy-Harvesting Techniques 125
3.10.1 Fundamentals of Wireless Energy Harvesting 126
3.10.2 Wireless Powered Communications 129
3.10.3 Full-Duplex Wireless-Powered Communication Network 131
3.10.4 Wireless Power Transfer in Cellular Networks 133
3.10.4.1 The Outage Constraint at BSs 134
3.10.4.2 The Power Outage Constraint at PBs 135
3.10.4.3 Hybrid Network Mobiles with Large Energy Storage 135
3.10.4.4 Hybrid Network Mobiles with Small Energy Storage 135
3.10.5 Harvested Energy Calculation 136
3.10.5.1 Energy Harvested from a FD BS (configuration 1) 136
3.10.5.2 Energy Harvested from PBs (configuration 2) 137
3.11 Integrated Energy and Spectrum Harvesting for 5G Communications 138
3.12 Energy and Spectrum Harvesting Cooperative Sensing Multiple Access Control (MAC) Protocol 140
3.13 Millimetre Wave (mmWave) Energy Harvesting 141
3.13.1 mmWave Network Model 141
3.13.2 mmWave Channel Model 142
3.13.3 Antenna Model 143
3.14 Analysis of mmWave Energy-Harvesting Technique 144
3.14.1 Connected User Case 145
3.15 Summary 145
References 146
Further Reading 148
4 5G Enabling Technologies: Narrowband Internet of Things and Smart Cities 151
4.1 Introduction to the Internet of Things (IoT) 151
4.2 IoT Architecture 152
4.2.1 Provisioning and Authentication 153
4.2.2 Configuration and Control 153
4.2.3 Monitoring and Diagnostics 153
4.2.4 Software Updates and Maintenance 154
4.3 Layered IoT Architecture 154
4.4 IoT Security Issues 155
4.5 Narrowband IoT 155
4.5.1 NB-IoT Modes of Operation 155
4.5.2 NB-IoT Transmission Options 156
4.5.2.1 DL Transmission Method 156
4.5.2.2 UL Transmission Method 156
4.6 DL Narrowband Physical Channels and Reference Signals 156
4.6.1 DL Physical Broadcast Channel (DPBCH) 156
4.6.2 Repetition Code SNR Gain Analysis 158
4.6.3 Narrowband Physical DL Shared Channel (NPDSCH) and Control Channel (NPDCCH) 159
4.6.4 Narrowband Reference Signal (NRS) 160
4.6.5 NB-IoT Primary Synchronization Signal (NPSS) 160
4.6.6 NB-IoT Secondary Synchronization Signal (NSSS) 163
4.6.7 Narrowband Positioning Reference Signal (NPRS) 165
4.7 UL Narrowband Physical Channels and Reference Signals 169
4.7.1 Narrowband Physical UL Shared Channel (NPUSCH) 169
4.7.2 Narrowband Physical Random-Access Channel (NPRACH) 170
4.7.3 Demodulation Reference Signals 172
4.7.3.1 DMRS Sequence for NPUSCH Format1 172
4.7.3.2 DMRS Sequence for NPUSCH Format2 173
4.8 NB-IoT System Design 174
4.8.1 LTE System Specifications 174
4.8.2 Bandwidth Perspective-Effective BW 175
4.8.2.1 Capacity Extension Consideration 175
4.8.2.2 Coverage Extension Consideration 176
4.8.3 Battery Usage Efficiency 177
4.9 Smart Cities 179
4.10 EU Smart City Model 180
4.10.1 Smart Economy 180
4.10.2 Smart Mobility 180
4.10.3 Smart Environment 181
4.10.4 Smart People 181
4.10.5 Smart Living 182
4.10.6 Smart Governance 183
4.11 Summary 184
4.A Minimum Time Required to Transmit Message M When B→∞ 185
References 186
Further Reading 188
5 Millimetre Wave Massive MIMO Technology 189
5.1 Introduction 189
5.2 Capacity of Point-to-Point MIMO Systems 190
5.2.1 Capacity of SIMO/MISO Links 190
5.2.2 Capacity of MIMO Links 190
5.3 Outage of Point-to-Point MIMO Links 193
5.4 Diversity-Multiplexing Tradeoffs 194
5.5 Multi-User-MIMO (MU-MIMO) Single-Cell Systems 195
5.5.1 UL Channel Capacity 196
5.5.2 DL Channel Capacity 196
5.6 Multi-User MIMO Multi-Cell System Representation 197
5.7 Sum Capacity of Broadcast Channels 198
5.7.1 Degraded BC 198
5.7.2 Nondegraded Gaussian Vector BC 200
5.7.3 MIMO BC Sum Capacity Using DPC 201
5.7.4 DPC Scheme Research Development for Application in the MIMO BC 205
5.7.5 Review of the DPC Scheme for Massive MIMO Systems 206
5.8 mmWave Massive MIMO Systems 206
5.8.1 Introduction 206
5.8.2 Reciprocity Model for Point-to-Point Links 208
5.8.3 Reciprocity Analysis 208
5.8.4 Reciprocity Analysis Extension to Multiple Users 209
5.8.5 Reciprocity and Pilot Contamination 210
5.9 MIMO Beamforming Schemes 210
5.9.1 Introduction to Beamforming 210
5.9.2 Analysis of Beamforming 210
5.10 BF Schemes 212
5.10.1 The Delay and Sum BF 212
5.10.2 Null Steering Beamformers 213
5.10.3 Beamformer Using a Reference Signal 214
5.11 mmWave BF Systems 215
5.11.1 Introduction 215
5.11.2 Hybrid Digital and Analogue BF for mmWave Antenna Arrays 216
5.12 Massive MIMO Hardware 221
5.13 mmWave Market and Choice of Technologies 226
5.14 Summary 227
5.A Derivation of Eq. (5.14) for M = 3, N = 2 229
5.B MUSIC Algorithm Used in Estimating the Direction of Signal Arrival 230
5.B.1 Introduction 230
5.B.2 MUSIC Algorithm for Estimating 1D Array AOAs 230
5.B.3 MUSIC Algorithm for Estimating 1D Linear Hybrid Array AOAs 233
5.B.4 MUSIC Algorithm for Estimating 2D Array AOAs. 234
References 236
6 mmWave Propagation Modelling: Atmospheric Gaseous and Rain Losses 241
6.1 Introduction 241
6.2 Contemporary Radio Wave Propagation Models 242
6.2.1 AT&T Propagation Model 243
6.2.2 Stanford University Interim (SUI) Propagation Model 244
6.2.3 Modified SUI Model for mmWave Propagation 245
6.3 Atmospheric Gaseous Losses 249
6.3.1 Introduction 249
6.3.2 Attenuation by Atmospheric Gases 250
6.3.3 ITU Recommendations for Modelling Atmospheric Gaseous Attenuation 252
6.3.4 Temperature and Pressure 254
6.3.5 Water-Vapour Pressure 254
6.4 Dry Atmosphere for Attenuation Calculations 256
6.5 Calculation of Atmospheric Gaseous Attenuation Using ITU-R
Recommendations 256
6.6 Rain Attenuation at mmWave Frequency Bands 257
6.6.1 Introduction 257
6.6.2 Research Development 258
6.7 The Physical Rain (EXCELL) Capsoni Model 259
6.7.1 Model Cells 260
6.7.2 Monoaxial Cell and Biaxial Cell Models 261
6.7.3 Fitting the Model to the Local Meteorological Data 261
6.7.4 Development of the Capsoni EXCELL Model 263
6.8 ITU Recommendations on Rainfall Rate Conversion 265
6.8.1 Introduction 265
6.8.2 Recommendations ITU-R P.530-17 and ITU-R P.838-3 266
6.8.2.1 Linear and Circular Polarization 266
6.8.3 Recommendations ITU-R P.1144-6 and ITU-R P.837-7 269
6.8.4 Recommendation ITU R P.1510-1 271
6.9 Attenuation from Snow and Hail 272
6.9.1 EM Propagation Properties Through Snow 272
6.9.2 Transmission Model for Ice Slab 277
6.9.3 Empirical Model for Snow Attenuation 278
6.9.4 Strong Fluctuation Theory 281
6.10 Snow Dielectric Constant Formulation Using Strong Fluctuation Theory 281
6.11 Summary 282
6.A Bilinear Interpolation 283
References 285
7 mmWave Propagation Modelling -Weather, Vegetation, and Building Material Losses 289
7.1 Introduction 289
7.2 Attenuation Due to Clouds and Fog 290
7.3 The Microphysical Modelling 290
7.4 Modified Gamma Droplets Size Distribution 292
7.4.1 Analysis of the Size Distribution 292
7.4.2 Skewness and Kurtosis of Modified Gamma Distribution 294
7.5 Rayleigh and Mie Scattering Distributions 297
7.6 ITU Empirical Model for Clouds and Fog Attenuation Calculation 298
7.7 Building Material Attenuation 300
7.7.1 Penetration Losses for Various Building Materials 300
7.7.2 Penetration Losses for Indoor Obstructions in an Office Environment at 28 GHz 301
7.7.3 The Penetration Loss for the Exterior of the House 301
7.8 Modelling the Penetration Loss for Building Materials 302
7.9 Modelling the Penetration Loss for Indoor Environments 302
7.10 Attenuation of Propagated Radio Waves in Vegetation 303
7.10.1 Foliage Propagation Path Models 303
7.10.2 Review of Horizontal Empirical Models 304
7.10.3 Weissberger MED Vegetation Loss Model 304
7.10.4 Recommendation ITU Vegetation Loss Model 305
7.10.5 The Maximum Attenuation (MA) Vegetation Loss Model 305
7.10.6 The Modified and Fitted ITU-R (MITU-R) and (FITU-R) Vegetation Loss Models 307
7.10.7 The COST235 Model 308
7.10.8 The Nonzero Gradient (NZG) Vegetation Loss Model 308
7.10.9 The Dual-Gradient (DG) Vegetation Loss Model 310
7.10.10 Indoor Vegetation Attenuation Measurement 312
7.11 Review of Vegetation Loss Using Empirical Models for Slant Propagation Path 312
7.12 Microphysical Modelling of Vegetation Attenuation 315
7.13 Attenuation in Vegetation Due to Diffraction 321
7.14 Recommendation ITU-R 526-7 321
7.15 Propagation Modes Connected with the Vegetation Foliage 322
7.15.1 Calculation of the Attenuation of the Top Diffracted Component 323
7.15.2 Attenuation Components Due to Side Diffraction 324
7.15.3 Attenuation of the Ground Reflection Component 325
7.15.4 Attenuation of the ‘Through’ or Scattered Component 326
7.15.5 Combination of the Individual Attenuation Components 326
7.16 Radiative Energy Transfer (RET)Theory 327
7.16.1 Introduction 327
7.16.2 RET Attenuation Prediction Model 329
7.16.2.1 Scattering Loss for Slant Radiation 331
7.16.2.2 Scattering Loss for Normal Radiation 332
7.16.3 Determination of the Medium-Dependent Parameters from Measurement Data 333
7.17 Summary 336
7.A Lognormal Distributed Random Numbers 336
7.B Derivation of Cloud Water Droplets Mode Radius 338
7.C The Complex Relative Permittivity and the Complex Relative Refractive Index Relationship 339
7.D Step-by-Step Tutorial to Calculate the Excess Through (Scatter) Loss in Vegetation 340
References 342
8 Wireless Channel Modelling and Array Mutual Coupling 347
8.1 Key Parameters in Wireless Channel Modelling 347
8.1.1 Doppler Spread 347
8.1.2 Coherence Time 348
8.1.3 Delay Spread 349
8.1.4 Coherence Bandwidth 350
8.2 Signal Fading 351
8.2.1 Small-Scale Fading Channels 351
8.2.1.1 Slow Fading 351
8.2.1.2 Fast Fading 351
8.2.1.3 Frequency Selective Fading 352
8.2.2 Large-Scale Fading Channels 352
8.2.3 Statistics of Wireless Channel 352
8.3 MIMO Channel Models 353
8.3.1 MIMO Channel Model Based on Perfect CSIT or CSIR 353
8.3.2 MIMO Channel Model Based on Perfect CSIR and CDIT 353
8.3.3 MIMO Channel Model Based on Perfect CDIT and CDIR 354
8.4 Massive MIMO Channel Models 355
8.4.1 i.i.d. Rayleigh Channel Model 355
8.5 Correlation Inspired Channel Models 356
8.5.1 Introduction 356
8.5.2 Formation of Kronecker Channel Model 359
8.6 Weichselberger Channel Model 360
8.6.1 Introduction 360
8.6.2 Formulation of Weichselberger Channel Model 362
8.7 Virtual Channel Representation 365
8.8 Mutual Coupling in Wireless Antenna Systems 367
8.8.1 Array Mutual Coupling 367
8.8.2 Mutual Coupling of Antenna Arrays Operating in Transmit and Receive Modes 368
8.8.3 BS Antennas Mutual Coupling in MIMO Systems 369
8.8.4 Total Power Collected by the Receiving Array 370
8.9 Mutual Coupling Constrained on Transmit Radiated Power 372
8.10 Analysis Voltage Induced at the Receive Antenna Port 372
8.11 MIMO Channel Capacity of Mutually Coupled Wireless Systems 374
8.11.1 Interference Consideration 374
8.11.2 Users Receiver Noise Consideration 375
8.11.3 Formulation of MIMO Channel Capacity 376
8.12 Summary 378
8.A S-Parameters 380
8.B Power Collected by the Receive Array is Maximum When S11 = SHRR 382
References 384
Further Reading 386
9 Massive Array Configurations and 3D Channel Modelling 387
9.1 Massive Antenna Array Configurations at BS 387
9.2 Uniform Linear Arrays 387
9.3 Rectangular Planar Arrays 388
9.4 Circular Arrays 388
9.5 Cylindrical Arrays 390
9.6 Spherical Antenna Arrays 391
9.7 Microstrip Patch Antennas 394
9.8 EU WINNER Projects 398
9.9 Spatial MIMO Channel Model in 3GPP Release 6 399
9.9.1 BS and MS Antenna Patterns 400
9.9.2 Per-Path BS and MS Angle Spread (AS) 400
9.9.3 Per-Path BS and MS Power Azimuth Spectrum 400
9.9.4 Definitions of BS and MS Angle Parameters for a Scattering Environment 402
9.10 The Scattering Environments 403
9.11 Large-Scale Parameters (LSPs) 403
9.11.1 Correlation Between Channel Parameters in 3GPP Release 6 405
9.11.2 Generation of Values of DS, AS, SF 405
9.12 2D Spatial Channel Models (SCMs) 407
9.12.1 Spatial Channel Models with No Antennas Polarization 407
9.12.2 Path Loss (PL) 407
9.12.3 2D Channel Coefficients 408
9.12.4 Generating Channel Parameters for Urban, Suburban Macrocell, and Urban Microcell Environments 408
9.13 2D Spatial Channel Models (SCMs) with Antenna Polarization 411
9.13.1 2D Spatial Channel Model (SCMs) with Polarized Antennas 412
9.14 3D Channel Models in 3GPP Release 14 413
9.14.1 Coordinate Systems 413
9.14.2 Local and Global Coordinate Systems 413
9.14.3 Scenarios Descriptions 416
9.14.4 Antenna Modelling 417
9.14.5 Probability of LOS 418
9.14.6 Estimate of the LOS Probability Using Ray Tracing 419
9.14.7 LOS Probability in 3GPP Release 14 420
9.14.8 Path Loss 422
9.14.8.1 UMacell Path Loss 422
9.14.8.2 LOS Channel Environment 422
9.14.8.3 Non-Line-of-Sight (NLOS) 422
9.14.9 Fast-Fading Model for 3D Channels 422
9.14.10 Large-Scale Parameters 424
9.14.11 Small-Scale Parameters 428
9.14.11.1 Channel Coefficients for NLOS Channel Environment 431
9.14.11.2 Channel Coefficients for LOS Channel Environment 432
9.14.11.3 Oxygen Absorption 433
9.14.11.4 Blockage Loss 433
9.15 Blockage Modelling 434
9.15.1 Blockages Modelling Using Random Shape Theory 434
9.15.2 Analysis Using Random Shape Theory to Model Buildings 436
9.15.3 Distance to Closest BS with Building Blockage 436
9.16 Summary 437
9.A Laplace Random Variables Distribution 438
9.B Spherical Coordinates 439
9.C Wrapped Gaussian Distribution 440
References 440
10 Massive MIMO Channel Estimation Schemes 443
10.1 Introduction 443
10.1.1 Cellular MIMO Channels 443
10.2 Massive MIMO Channels Definition 445
10.2.1 Massive MIMO UL Definition 445
10.3 Time-Division Duplexing (TDD) Transmission Protocol 447
10.4 Massive MIMO Channel Estimation in Noncooperative TDD Networks 447
10.4.1 Uplink Pilots’ Transmission Using the Aligned Pilot Scheme 448
10.4.2 SINR for Uplink Data Transmission 449
10.4.3 SINR for Downlink Data Transmission 450
10.4.4 Massive MIMO Channels Estimation Using Time-Shifted Pilot Scheme (TSPS) 451
10.5 Channel Estimation Using Coordinated Cells in MIMO System 454
10.5.1 Bayesian Estimation of Uplink for All Users 455
10.5.2 Bayesian Desired Channel Estimation with Full Pilot Reuse 458
10.6 Bayesian Estimation of UL in a Massive MIMO System 460
10.6.1 Rule of Coordinated Pilot Allocation 461
10.6.2 Evaluation of the Coordinated Pilot Assignment Protocol 461
10.7 Arbitrary Correlated Rician Fading Channel 465
10.7.1 Estimation of Correlated Rician Channels Using MMSE Approach 465
10.7.2 Pilot Sequence Optimization for Channel Matrix Estimation 467
10.7.3 Optimal Length of Pilot Sequences 468
10.8 Massive MIMO Antennas Calibration 469
10.8.1 Argos Method 470
10.8.2 Mutual Coupling Calibration Antennas Method 473
10.9 Pre-precoding/Post-precoding Channel Calibration 479
10.10 Summary 481
10.A Noncooperative TDD Networks: Derivation of the Asymptotic Normalization Factor Equation 482
10.B Beamforming Vectors for Time-Shifted Pilot Scheme 483
10.C Derivation of equations (10.48b) and (10.49b) 484
References 486
11 Linear Precoding Strategies for Multi-User Massive MIMO Systems 489
11.1 Introduction 489
11.2 Group-Level and Symbol-Level Precoding 490
11.3 Linear Precoding Schemes 491
11.4 SU-MIMO Model 492
11.5 Multi-User MIMO Precoding System Model 493
11.5.1 Broadcast Channel (BC) System Model 493
11.5.2 Multiple Access Channels (MAC) System Model with Non-Equal Antennas at Each User 494
11.5.3 Linear Precoding for Massive MIMO MAC with Equal Antennas at Each User 495
11.6 Linear Multi-User Transmit Channel Inversion Precoding for BC 496
11.7 Zero-Forcing Precoding using the Wiesel et al. Method 497
11.7.1 Multi-User Linear Zero-Forcing (ZF) Precoding for BC 497
11.7.2 ZF Precoder Design with Total Transmit Power Constraint 498
11.7.3 Optimal ZF Precoding with per-Antenna Power Constraint 499
11.8 The Outage Probability 500
11.9 Precoding for MIMO Channels with Johan et al. Method 502
11.9.1 Introduction 502
11.9.2 ZF Transmit Filter F Matrix 503
11.9.3 ZF Receive Filter E Matrix 504
11.9.4 ZF Outage Probability for Minimum Transmit Power 505
11.9.5 ZF Precoder Design to Allocate Unequal Power 505
11.9.6 ZF Outage Probability for Unequal Power Allocation across Transmit Antennas 506
11.10 Matched Filter (MF) Precoding 507
11.10.1 Transmit MF F Matrix 507
11.10.2 Receive MF E Matrix 507
11.11 Wiener Filter (WF) Precoding 509
11.11.1 Transmit WF F Matrix 509
11.11.2 Receive WF Matrix 510
11.12 Regularized Zero-Forcing (RZF) Precoding 511
11.13 Block Diagonalization (BD) 514
11.13.1 Multi-User BD Precoding 514
11.13.2 BD Transmit Filter and Receive Filter Matrices 515
11.14 Transmit MF Precoding Filters and MMSE Receive Filters in MIMO Broadcast Channel 519
11.15 Linear Precoding Based on Truncated Polynomial Expansion 520
11.15.1 Introduction 520
11.15.2 Modelling the TPE Precoding for BC 521
11.16 Summary 525
11.A Derivation of the Scaling Factor 𝛽ZF 527
11.B ZF Precoder Design Optimum User Power in Unequal Power Allocation 527
11.C Transmit Matched Filter (MF) Precoding 529
11.D Wiener Filter (WF) Precoding 530
11.E MMSE Matrix 532
11.F SINR for MMSE Receiver for MF the Transmit Precoding 534
References 535
Index 539
