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5G Physical Layer Technologies
MA Abu–Rgheff (Author)
9781119525516, Wiley
Hardback, published 8 November 2019
592 pages
25.4 x 17.8 x 3.1 cm, 1.352 kg
5G Physical Layer Technologies 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: 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.
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 1.4 Multiuser Wireless Network Capacity Regions 10 1.5 Fading Wireless Channels 19 1.6 Multicell MIMO Channels 20 1.7 Green Wireless Communications for the Twenty-First Century 20 1.8 BS Power Model 25 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 2.5 Cache-Enabled Small-Cell Networks (CE-SCNs) 57 2.6 Full-Duplex (FD) Communications 61 2.7 Review of Reference Signals, Antenna Ports, and Channels 74 2.8 Full-Dimension MIMO Technology 79 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 3.3 Evolved Resource Sharing 107 3.4 Network Functions Virtualization (NFV) 113 3.5 vRAN Supporting Fronthaul 117 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 3.11 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 3.14 Analysis of mmWave Energy-Harvesting Technique 144 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 4.3 Layered IoT Architecture 154 4.4 IoT Security Issues 155 4.5 Narrowband IoT 155 4.6 DL Narrowband Physical Channels and Reference Signals 156 4.7 UL Narrowband Physical Channels and Reference Signals 169 4.8 NB-IoT System Design 174 4.9 Smart Cities 179 4.10 EU Smart City Model 180 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 5.3 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 5.6 Multi-User MIMO Multi-Cell System Representation 197 5.7 Sum Capacity of Broadcast Channels 198 5.8 mmWave Massive MIMO Systems 206 5.9 MIMO Beamforming Schemes 210 5.10 BF Schemes 212 5.11 mmWave BF Systems 215 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 6.3 Atmospheric Gaseous Losses 249 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 6.7 The Physical Rain (EXCELL) Capsoni Model 259 6.8 ITU Recommendations on Rainfall Rate Conversion 265 6.9 Attenuation from Snow and Hail 272 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 7.5 Rayleigh and Mie Scattering Distributions 297 7.6 ITU Empirical Model for Clouds and Fog Attenuation Calculation 298 7.7 Building Material Attenuation 300 7.8 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 7.11 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 7.16 Radiative Energy Transfer (RET)Theory 327 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 8.2 Signal Fading 351 8.3 MIMO Channel Models 353 8.4 Massive MIMO Channel Models 355 8.5 Correlation Inspired Channel Models 356 8.6 Weichselberger Channel Model 360 8.7 Virtual Channel Representation 365 8.8 Mutual Coupling in Wireless Antenna Systems 367 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 8.12 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 9.10 The Scattering Environments 403 9.11 Large-Scale Parameters (LSPs) 403 9.12 2D Spatial Channel Models (SCMs) 407 9.14 3D Channel Models in 3GPP Release 14 413 9.15 Blockage Modelling 434 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 10.2 Massive MIMO Channels Definition 445 10.3 Time-Division Duplexing (TDD) Transmission Protocol 447 10.4 Massive MIMO Channel Estimation in Noncooperative TDD Networks 447 10.5 Channel Estimation Using Coordinated Cells in MIMO System 454 10.6 Bayesian Estimation of UL in a Massive MIMO System 460 10.7 Arbitrary Correlated Rician Fading Channel 465 10.8 Massive MIMO Antennas Calibration 469 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 11.6 Linear Multi-User Transmit Channel Inversion Precoding for BC 496 11.7 Zero-Forcing Precoding using the Wiesel et al. Method 497 11.8 The Outage Probability 500 11.9 Precoding for MIMO Channels with Johan et al. Method 502 11.10 Matched Filter (MF) Precoding 507 11.11 Wiener Filter (WF) Precoding 509 11.12 Regularized Zero-Forcing (RZF) Precoding 511 11.13 Block Diagonalization (BD) 514 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 11.16 Summary 525 11.A Derivation of the Scaling Factor ;;ZF 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
Subject Areas: Electronics & communications engineering [TJ]
