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5G Physical Layer Technologies

Mosa Ali Abu-Rgheff

ISBN: 978-1-119-52551-6 October 2019 Wiley-IEEE Press 592 Pages

Description

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.

Preface

Acknowledgements

List of Mathematical Notation

List of Wireless Network Symbols 

List of Abbreviations

1 Introduction

1.1 Motivations

1.2 Overview of contemporary cellular wireless networks

1.3 Evolution of wireless communications in 3GPP Releases

1.4 Multiuser wireless network Capacity regions

1.5 Fading wireless channels

1.6 Multicell MIMO Channels

1.7 Green wireless communications for 21st century

1.8 BS power model

1.9 Green cellular networks

1.10 Green heterogeneous Networks

1.11 Summary

References

Tutorial on theory and techniques of optimization mathematics 

1A Basics

1B Theory of optimization mathematics

1C Karush–Kuhn–Tucker (KKT) conditions

2 Small cells; Full Duplex Communications; and Full Dimension MIMO Technologies

2.1 Introduction

2.2 The Rationale for 5G Enabling Technologies

2.3 Network densification

2.4 Cloud-Based Radio Access Network (C-RAN)

2.5 Cache-enabled Small Cell Network (CE-SCN)

2.6 Full duplex (FD) communications

2.7 Review of reference signals, antenna ports and channels

2.8 Full Dimension MIMO Technology

2.9 Summary

References

Bibliography

Appendices

2A Notes on Q-learning algorithm

2B Outage probability in CE-SC Networks

2C Signal power at the receive antenna after antenna cancellation of self interference

3 Network Virtualization and Wireless Energy Harvesting

3.1 Introduction

3.2 Network sharing and virtualization of wireless resources

3.3 Evolved resource sharing

3.4 Network Functions Virtualization (NFV)

3.5 vRAN supporting fronthaul

3.6 Virtual Evolved Packet Core (vEPC)

3.7 Virtualized switches 'Wireless powered Communication: Opportunities and Challenges'

3.8 Auction in resource provisions

3.9 Hierarchical combinatorial auction models

3.10 Energy Harvesting Techniques

3.11 Integrated Energy and Spectrum Harvesting for 5G Communications

3.12 Energy and spectrum harvesting cooperative sensing multiple access control (MAC) protocol

3.13 Millimeter Wave Energy harvesting

3.14 Analysis of mmWave energy harvesting technique

3.15 Summary

References

Bibliography

4 Narrowband Internet of Things and Smart cities

4.1 Introduction Internet of Things (IoT)

4.2. IoT Architecture

4.3 Layered IoT Architecture

4.4 IoT Security issues

4.5 Narrowband IoT

4.6 DL narrowband physical channels and reference signals

4.7 UL Narrowband physical channels and reference signals

4.8 NB-IoT system Design

4.9 Smart cities

4.10 EU smart City Model

4.11 Summary

References

Bibliography

Appendix 4A minimum time required to transmit message M when B→∞

5 Millimeter Wave Massive MIMO Technology

5.1 Introduction

5.2 Capacity of point to point MIMO systems

5.3 Outage of point to point MIMO link

5.4 Diversity-multiplexing tradeoffs

5.5 Multiuser-MIMO (MU-MIMO) single cell system

5.6 Multi-User MIMO Multi – Cell system representation

5.7 Sum Capacity of Broadcast channels

5.8 mmWave Massive MIMO System

5.9 MIMO beamforming (BF) Schemes

5.10 BF schemes:

5.11 mmWave BF systems

5.12 Massive MIMO Hardware

5.13 mm-Wave market and choice of technologies

5.14 Summary

References

Appendices

5A Derivation of (15) for M = 3, N = 2

5B MUSIC Algorithm used in estimating the direction of signal arrival

6 Atmospheric Gaseous and Rain Losses

6.1 Introduction

6.2 Contemporary Radio Wave Propagation Models

6.3 Atmospheric Gaseous Losses

6.4 Dry atmosphere for attenuation calculations

6.5 Calculation of atmospheric gaseous attenuation using ITU-R recommendations

6.6 Rain attenuation at mm wave frequency bands

6.7 The Physical Rain (EXCELL) Capsoni Model

6.8 The ITU recommendations on rainfall rate conversion

6.9 Attenuation by snow and hails

6.10 Snow dielectric constant formulation using strong fluctuation theory

6.11 Summary

References

Appendix Bi-linear interpolation

7 Weather; Vegetation; and Building Material Losses

7.1 Introduction

7.2 Attenuation due to Clouds and Fog

7.3 The microphysical modeling

7.4 Modified Gamma droplets size distribution

7.5 Rayleigh and Mie scattering distributions

7.6 ITU empirical model for clouds and fog attenuation calculation

7.7 Building material attenuation

7.8 Modelling the penetration loss for building materials

7.9 Modelling the penetration loss for indoor environment.

7.10 Attenuation of propagated radio waves in Vegetation

7.11 Attenuation in vegetation due to diffraction

7.12 Radiative Energy Transfer Theory (RET)

7.13 Summary

References

Appendices

7A Lognormal distributedrandom numbers

7B Derivation of cloud water droplets mode radius

7C The complex relative permittivity and the complex relative refractive index relationship.

7D A step by step tutorial on the calculation of the excess through (scatter) loss in vegetation

8 Wireless Channel Modelling and Array Mutual Coupling

8.1 Key parameters in wireless channel modelling

8.2 channel fading

8.3 MIMO Wireless Channel models

8.4 MassiveMIMO channel models

8.5 Correlation inspired Channel Models:

8.6 Weichselberger channel model

8.7 Virtual channel Representation

8.8 Mutual Coupling in Wireless Antenna Systems.

8.9 Mutual Coupling Constrained on transmit radiated power

8.10 Analysis of the voltage induced at the receive antenna port

8.11 MIMO Channel Capacity of Mutual Coupled Wireless Systems

8.12 Summary

References

Bibliography

Appendices

8A S-Parameters

8B Powercollected by the receive array is maximum when S11 = SHRR

9 Massive Array Configurations and 3D Channel Modelling

9.1 Massive antenna arrays configuration at BS

9.2 Uniform linear arrays

9.3 Rectangular planar arrays

9.4 Circular arrays

9.5 Cylindrical arrays

9.6 Spherical antenna array

9.7 Microstrip patch antennas

9.8 EU WINNER Projects

9.9 Spatial MIMO channel model in 3GPP Release 6

9.10 The Scattering environments

9.11 Large Scale parameters (LSPs) in 3GPP Release 6

9.12 2D Spatial Channel models (SCMs)

9.13 2D Spatial Channel Models with Antenna Polarization

9.14 3D channel models in 3GPP release 14

9.15 Blockages modelling

9.16 Summary

References

Bibliography

Appendices

9A Laplace random variables Distribution

9B Spherical coordinates

9C Wrapped Gaussian distribution

10 Massive MIMO Channel Estimation Schemes

10 Introduction

10.1 Cellular MIMO channels

10.2 Massive MIMO channels definition

10.3 Time-division duplexing (TDD) transmission protocol

10.4 Massive MIMO channels estimation in non-cooperative TDD networks

10.5 Channel estimation using coordinated cells in MIMO system

10.6 Bayesian estimation of UL channel in a massive MIMO system

10.7 Arbitrary correlated Ricean fading channel

10.8 Massive MIMO channel calibration

10.9 Pre-precoding / post -precoding channel calibration

10.10 Summary

References

Appendices

10A Non-cooperative TDD networks: Derivation of the asymptotic normalization factor equation (12)

10B Beamforming vectors for time-shifted pilot scheme

10C Derivation of equations (49b) and (50b)

11 Linear Precoding Strategies for Multiuser Massive MIMO Systems

11.1 Introduction

11.2 Group level/ symbol level precoding

11.3 Linear precoding schemes

11.4 SU-MIMO precoding Model

11.5 Multiuser MIMO precoding system model

11.6 Linear multiuser Transmit Channel inversion precoding for BC

11.7 Zero forcing (ZF) precoding [Wiesel et al method]

11.8 The Outage probability

11.9 Precoding for MIMO channels [Joham et al method]

11.10 Matched filter (MF) precoding

11.11 Wiener Filter (WF) precoding

11.12 Regularized Zero-Forcing (RZF) precoding

11.13 Block Diagonalization (BD)

11.14 Transmit MF precoding filter and MMSE receive filter in MIMO Broadcast channel

11.15 Linear precoding based on truncated polynomial expansion

11.16 Summary

References

Appendices

11A Derivation of the scaling factor βZF

11B ZF precoder design optimum user power in unequal power allocation

11C Transmit Matched Filter (MF) precoding

11D Wiener Filter (WF) precoding

11E MMSE Matrix

11F SINR for MMSE receiver for MF the transmit precoding

Index