Machine Learning for Future Wireless Communications
1st Edition
Published on 13. February 2020
Book
Hardback
496 pages
978-1-119-56225-2 (ISBN)
Description
A comprehensive review to the theory, application and research of machine learning for future wireless communications
In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities.
Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author - a noted expert on the topic - covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource:
Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks
Covers a range of topics from architecture and optimization to adaptive resource allocations
Reviews state-of-the-art machine learning based solutions for network coverage
Includes an overview of the applications of machine learning algorithms in future wireless networks
Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing
Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities.
Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author - a noted expert on the topic - covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource:
Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks
Covers a range of topics from architecture and optimization to adaptive resource allocations
Reviews state-of-the-art machine learning based solutions for network coverage
Includes an overview of the applications of machine learning algorithms in future wireless networks
Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing
Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
More details
Series
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 258 mm
Width: 187 mm
Thickness: 32 mm
Weight
1032 gr
ISBN-13
978-1-119-56225-2 (9781119562252)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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E-Book
12/2019
1st Edition
Wiley
€134.99
Available for download
E-Book
12/2019
1st Edition
Wiley
€134.99
Available for download
Person
FA-LONG LUO, Ph.D, Silicon Valley, California, USA
Dr. Fa-Long Luo is an IEEE Fellow and an Affiliate Full Professor of Electrical & Computer Engineering Department at the University of Washington in Seattle. Having gained international high recognition, Dr. Luo has 36 years of research and industry experience in wireless communication, neural networks, signal processing, machine learning and broadcasting with real-time implementation, applications and standardization. Including his well-received book: Signal Processing for 5G: Algorithms and Implementations (2016, Wiley-IEEE), Dr. Luo has published 6 books and more than 100 technical papers in the related fields. Dr. Luo has also contributed 61 patents/inventions which have successfully resulted in a number of new or improved commercial products in mass production. He has served as the Chairman of IEEE Industry DSP Standing Committee and the Technical Board Member of Signal Processing Society. Dr. Luo was awarded the Fellowship by the Alexander von Humboldt Foundation of Germany.
Dr. Fa-Long Luo is an IEEE Fellow and an Affiliate Full Professor of Electrical & Computer Engineering Department at the University of Washington in Seattle. Having gained international high recognition, Dr. Luo has 36 years of research and industry experience in wireless communication, neural networks, signal processing, machine learning and broadcasting with real-time implementation, applications and standardization. Including his well-received book: Signal Processing for 5G: Algorithms and Implementations (2016, Wiley-IEEE), Dr. Luo has published 6 books and more than 100 technical papers in the related fields. Dr. Luo has also contributed 61 patents/inventions which have successfully resulted in a number of new or improved commercial products in mass production. He has served as the Chairman of IEEE Industry DSP Standing Committee and the Technical Board Member of Signal Processing Society. Dr. Luo was awarded the Fellowship by the Alexander von Humboldt Foundation of Germany.
Content
List of Contributors xv
Preface xxi
Part I Spectrum Intelligence and Adaptive Resource Management 1
1 Machine Learning for Spectrum Access and Sharing 3
Kobi Cohen
1.1 Introduction 3
1.2 Online Learning Algorithms for Opportunistic Spectrum Access 4
1.3 Learning Algorithms for Channel Allocation 9
1.4 Conclusions 19
Acknowledgments 20
Bibliography 20
2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 27
Andres Kwasinski, Wenbo Wang, and Fatemeh Shah Mohammadi
2.1 Use of Q-Learning for Cross-layer Resource Allocation 29
2.2 Deep Q-Learning and Resource Allocation 33
2.3 Cooperative Learning and Resource Allocation 36
2.4 Conclusions 42
Bibliography 43
3 Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks 45
Hadi Ghauch, Hossein Shokri-Ghadikolaei, Gabor Fodor, Carlo Fischione, and Mikael Skoglund
3.1 Background and Motivation 45
3.2 System Model and Problem Formulation 49
3.3 Hybrid Solution Approach 54
3.4 Conclusions and Discussions 59
Appendix A Appendix for Chapter 3 61
A.1 Overview of Reinforcement Learning 61
Bibliography 61
4 Deep Learning-Based Coverage and Capacity Optimization 63
Andrei Marinescu, Zhiyuan Jiang, Sheng Zhou, Luiz A. DaSilva, and Zhisheng Niu
4.1 Introduction 63
4.2 Related Machine Learning Techniques for Autonomous Network Management 64
4.3 Data-Driven Base-Station Sleeping Operations by Deep Reinforcement Learning 67
4.4 Dynamic Frequency Reuse through a Multi-Agent Neural Network Approach 72
4.5 Conclusions 81
Bibliography 82
5 Machine Learning for Optimal Resource Allocation 85
Marius Pesavento and Florian Bahlke
5.1 Introduction and Motivation 85
5.2 System Model 88
5.3 Resource Minimization Approaches 90
5.4 Numerical Results 96
5.5 Concluding Remarks 99
Bibliography 100
6 Machine Learning in Energy Efficiency Optimization 105
Muhammad Ali Imran, Ana Flavia dos Reis, Glauber Brante, Paulo Valente Klaine, and Richard Demo Souza
6.1 Self-Organizing Wireless Networks 106
6.2 Traffic Prediction and Machine Learning 110
6.3 Cognitive Radio and Machine Learning 111
6.4 Future Trends and Challenges 112
6.5 Conclusions 114
Bibliography 114
7 Deep Learning Based Traffic and Mobility Prediction 119
Honggang Zhang, Yuxiu Hua, Chujie Wang, Rongpeng Li, and Zhifeng Zhao
7.1 Introduction 119
7.2 Related Work 120
7.3 Mathematical Background 122
7.4 ANN-Based Models for Traffic and Mobility Prediction 124
7.5 Conclusion 133
Bibliography 134
8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing 137
Benjamin Sliwa, Robert Falkenberg, and Christian Wietfeld
8.1 Mobile Crowdsensing 137
8.2 ML-Based Context-Aware Data Transmission 140
8.3 Methodology for Real-World Performance Evaluation 148
8.4 Results of the Real-World Performance Evaluation 149
8.5 Conclusion 152
Acknowledgments 154
Bibliography 154
Part II Transmission Intelligence and Adaptive Baseband Processing 157
9 Machine Learning-Based Adaptive Modulation and Coding Design 159
Lin Zhang and Zhiqiang Wu
9.1 Introduction and Motivation 159
9.2 SL-Assisted AMC 162
9.3 RL-Assisted AMC 172
9.4 Further Discussion and Conclusions 178
Bibliography 178
10 Machine Learning-Based Nonlinear MIMO Detector 181
Song-Nam Hong and Seonho Kim
10.1 Introduction 181
10.2 A Multihop MIMO Channel Model 182
10.3 Supervised-Learning-based MIMO Detector 184
10.4 Low-Complexity SL (LCSL) Detector 188
10.5 Numerical Results 191
10.6 Conclusions 193
Bibliography 193
11 Adaptive Learning for Symbol Detection: A Reproducing Kernel Hilbert Space Approach 197
Daniyal Amir Awan, Renato Luis Garrido Cavalcante, Masahario Yukawa, and Slawomir Stanczak
11.1 Introduction 197
11.2 Preliminaries 198
11.3 System Model 200
11.4 The Proposed Learning Algorithm 203
11.5 Simulation 207
11.6 Conclusion 208
Appendix A Derivation of the Sparsification Metric and the Projections onto the Subspace Spanned by the Nonlinear Dictionary 210
Bibliography 211
12 Machine Learning for Joint Channel Equalization and Signal Detection 213
Lin Zhang and Lie-Liang Yang
12.1 Introduction 213
12.2 Overview of Neural Network-Based Channel Equalization 214
12.3 Principles of Equalization and Detection 219
12.5 Performance of OFDM Systems With Neural Network-Based Equalization 232
12.6 Conclusions and Discussion 236
Bibliography 237
13 Neural Networks for Signal Intelligence: Theory and Practice 243
Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, and Tommaso Melodia
13.1 Introduction 243
13.2 Overview of Artificial Neural Networks 244
13.3 Neural Networks for Signal Intelligence 248
13.4 Neural Networks for Spectrum Sensing 255
13.5 Open Problems 259
13.6 Conclusion 260
Bibliography 260
14 Channel Coding with Deep Learning: An Overview 265
Shugong Xu
14.1 Overview of Channel Coding and Deep Learning 265
14.2 DNNs for Channel Coding 268
14.3 CNNs for Decoding 277
14.4 RNNs for Decoding 279
14.5 Conclusions 283
Bibliography 283
15 Deep Learning Techniques for Decoding Polar Codes 287
Warren J. Gross, Nghia Doan, Elie Ngomseu Mambou, and Seyyed Ali Hashemi
15.1 Motivation and Background 287
15.2 Decoding of Polar Codes: An Overview 289
15.3 DL-Based Decoding for Polar Codes 292
15.4 Conclusions 299
Bibliography 299
16 Neural Network-Based Wireless Channel Prediction 303
Wei Jiang, Hans Dieter Schotten, and Ji-ying Xiang
16.1 Introduction 303
16.2 Adaptive Transmission Systems 305
16.3 The Impact of Outdated CSI 307
16.4 Classical Channel Prediction 309
16.5 NN-Based Prediction Schemes 313
16.6 Summary 323
Bibliography 323
Part III Network Intelligence and Adaptive System Optimization 327
17 Machine Learning for Digital Front-End: a Comprehensive Overview 329
Pere L. Gilabert, David Lopez-Bueno, Thi Quynh Anh Pham, and Gabriel Montoro
17.1 Motivation and Background 329
17.2 Overview of CFR and DPD 331
17.3 Dimensionality Reduction and ML 341
17.4 Nonlinear Neural Network Approaches 350
17.5 Support Vector Regression Approaches 368
17.6 Further Discussion and Conclusions 373
Bibliography 374
18 Neural Networks for Full-Duplex Radios: Self-Interference Cancellation 383
Alexios Balatsoukas-Stimming
18.1 Nonlinear Self-Interference Models 384
18.2 Digital Self-Interference Cancellation 386
18.3 Experimental Results 391
18.4 Conclusions 393
Bibliography 395
19 Machine Learning for Context-Aware Cross-Layer Optimization 397
Yang Yang, Zening Liu, Shuang Zhao, Ziyu Shao, and Kunlun Wang
19.1 Introduction 397
19.2 System Model 399
19.3 Problem Formulation and Analytical Framework 402
19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm 409
19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks 413
19.6 Conclusion 420
Bibliography 421
20 Physical-Layer Location Verification by Machine Learning 425
Stefano Tomasin, Alessandro Brighente, Francesco Formaggio, and Gabriele Ruvoletto
20.1 IRLV by Wireless Channel Features 427
20.2 ML Classification for IRLV 428
20.3 Learning Phase Convergence 431
20.4 Experimental Results 433
20.5 Conclusions 437
Bibliography 437
21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439
M. Cenk Gursoy, Chen Zhong, and Senem Velipasalar
21.1 Introduction 439
21.2 System Model 441
21.3 Problem Formulation 443
21.4 Deep Actor-Critic Framework for Content Caching 446
21.5 Application to the Multi-Cell Network 448
21.6 Application to the Single-Cell Network with D2D Communications 452
21.7 Conclusion 454
Bibliography 455
Index 459
Preface xxi
Part I Spectrum Intelligence and Adaptive Resource Management 1
1 Machine Learning for Spectrum Access and Sharing 3
Kobi Cohen
1.1 Introduction 3
1.2 Online Learning Algorithms for Opportunistic Spectrum Access 4
1.3 Learning Algorithms for Channel Allocation 9
1.4 Conclusions 19
Acknowledgments 20
Bibliography 20
2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 27
Andres Kwasinski, Wenbo Wang, and Fatemeh Shah Mohammadi
2.1 Use of Q-Learning for Cross-layer Resource Allocation 29
2.2 Deep Q-Learning and Resource Allocation 33
2.3 Cooperative Learning and Resource Allocation 36
2.4 Conclusions 42
Bibliography 43
3 Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks 45
Hadi Ghauch, Hossein Shokri-Ghadikolaei, Gabor Fodor, Carlo Fischione, and Mikael Skoglund
3.1 Background and Motivation 45
3.2 System Model and Problem Formulation 49
3.3 Hybrid Solution Approach 54
3.4 Conclusions and Discussions 59
Appendix A Appendix for Chapter 3 61
A.1 Overview of Reinforcement Learning 61
Bibliography 61
4 Deep Learning-Based Coverage and Capacity Optimization 63
Andrei Marinescu, Zhiyuan Jiang, Sheng Zhou, Luiz A. DaSilva, and Zhisheng Niu
4.1 Introduction 63
4.2 Related Machine Learning Techniques for Autonomous Network Management 64
4.3 Data-Driven Base-Station Sleeping Operations by Deep Reinforcement Learning 67
4.4 Dynamic Frequency Reuse through a Multi-Agent Neural Network Approach 72
4.5 Conclusions 81
Bibliography 82
5 Machine Learning for Optimal Resource Allocation 85
Marius Pesavento and Florian Bahlke
5.1 Introduction and Motivation 85
5.2 System Model 88
5.3 Resource Minimization Approaches 90
5.4 Numerical Results 96
5.5 Concluding Remarks 99
Bibliography 100
6 Machine Learning in Energy Efficiency Optimization 105
Muhammad Ali Imran, Ana Flavia dos Reis, Glauber Brante, Paulo Valente Klaine, and Richard Demo Souza
6.1 Self-Organizing Wireless Networks 106
6.2 Traffic Prediction and Machine Learning 110
6.3 Cognitive Radio and Machine Learning 111
6.4 Future Trends and Challenges 112
6.5 Conclusions 114
Bibliography 114
7 Deep Learning Based Traffic and Mobility Prediction 119
Honggang Zhang, Yuxiu Hua, Chujie Wang, Rongpeng Li, and Zhifeng Zhao
7.1 Introduction 119
7.2 Related Work 120
7.3 Mathematical Background 122
7.4 ANN-Based Models for Traffic and Mobility Prediction 124
7.5 Conclusion 133
Bibliography 134
8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing 137
Benjamin Sliwa, Robert Falkenberg, and Christian Wietfeld
8.1 Mobile Crowdsensing 137
8.2 ML-Based Context-Aware Data Transmission 140
8.3 Methodology for Real-World Performance Evaluation 148
8.4 Results of the Real-World Performance Evaluation 149
8.5 Conclusion 152
Acknowledgments 154
Bibliography 154
Part II Transmission Intelligence and Adaptive Baseband Processing 157
9 Machine Learning-Based Adaptive Modulation and Coding Design 159
Lin Zhang and Zhiqiang Wu
9.1 Introduction and Motivation 159
9.2 SL-Assisted AMC 162
9.3 RL-Assisted AMC 172
9.4 Further Discussion and Conclusions 178
Bibliography 178
10 Machine Learning-Based Nonlinear MIMO Detector 181
Song-Nam Hong and Seonho Kim
10.1 Introduction 181
10.2 A Multihop MIMO Channel Model 182
10.3 Supervised-Learning-based MIMO Detector 184
10.4 Low-Complexity SL (LCSL) Detector 188
10.5 Numerical Results 191
10.6 Conclusions 193
Bibliography 193
11 Adaptive Learning for Symbol Detection: A Reproducing Kernel Hilbert Space Approach 197
Daniyal Amir Awan, Renato Luis Garrido Cavalcante, Masahario Yukawa, and Slawomir Stanczak
11.1 Introduction 197
11.2 Preliminaries 198
11.3 System Model 200
11.4 The Proposed Learning Algorithm 203
11.5 Simulation 207
11.6 Conclusion 208
Appendix A Derivation of the Sparsification Metric and the Projections onto the Subspace Spanned by the Nonlinear Dictionary 210
Bibliography 211
12 Machine Learning for Joint Channel Equalization and Signal Detection 213
Lin Zhang and Lie-Liang Yang
12.1 Introduction 213
12.2 Overview of Neural Network-Based Channel Equalization 214
12.3 Principles of Equalization and Detection 219
12.5 Performance of OFDM Systems With Neural Network-Based Equalization 232
12.6 Conclusions and Discussion 236
Bibliography 237
13 Neural Networks for Signal Intelligence: Theory and Practice 243
Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, and Tommaso Melodia
13.1 Introduction 243
13.2 Overview of Artificial Neural Networks 244
13.3 Neural Networks for Signal Intelligence 248
13.4 Neural Networks for Spectrum Sensing 255
13.5 Open Problems 259
13.6 Conclusion 260
Bibliography 260
14 Channel Coding with Deep Learning: An Overview 265
Shugong Xu
14.1 Overview of Channel Coding and Deep Learning 265
14.2 DNNs for Channel Coding 268
14.3 CNNs for Decoding 277
14.4 RNNs for Decoding 279
14.5 Conclusions 283
Bibliography 283
15 Deep Learning Techniques for Decoding Polar Codes 287
Warren J. Gross, Nghia Doan, Elie Ngomseu Mambou, and Seyyed Ali Hashemi
15.1 Motivation and Background 287
15.2 Decoding of Polar Codes: An Overview 289
15.3 DL-Based Decoding for Polar Codes 292
15.4 Conclusions 299
Bibliography 299
16 Neural Network-Based Wireless Channel Prediction 303
Wei Jiang, Hans Dieter Schotten, and Ji-ying Xiang
16.1 Introduction 303
16.2 Adaptive Transmission Systems 305
16.3 The Impact of Outdated CSI 307
16.4 Classical Channel Prediction 309
16.5 NN-Based Prediction Schemes 313
16.6 Summary 323
Bibliography 323
Part III Network Intelligence and Adaptive System Optimization 327
17 Machine Learning for Digital Front-End: a Comprehensive Overview 329
Pere L. Gilabert, David Lopez-Bueno, Thi Quynh Anh Pham, and Gabriel Montoro
17.1 Motivation and Background 329
17.2 Overview of CFR and DPD 331
17.3 Dimensionality Reduction and ML 341
17.4 Nonlinear Neural Network Approaches 350
17.5 Support Vector Regression Approaches 368
17.6 Further Discussion and Conclusions 373
Bibliography 374
18 Neural Networks for Full-Duplex Radios: Self-Interference Cancellation 383
Alexios Balatsoukas-Stimming
18.1 Nonlinear Self-Interference Models 384
18.2 Digital Self-Interference Cancellation 386
18.3 Experimental Results 391
18.4 Conclusions 393
Bibliography 395
19 Machine Learning for Context-Aware Cross-Layer Optimization 397
Yang Yang, Zening Liu, Shuang Zhao, Ziyu Shao, and Kunlun Wang
19.1 Introduction 397
19.2 System Model 399
19.3 Problem Formulation and Analytical Framework 402
19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm 409
19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks 413
19.6 Conclusion 420
Bibliography 421
20 Physical-Layer Location Verification by Machine Learning 425
Stefano Tomasin, Alessandro Brighente, Francesco Formaggio, and Gabriele Ruvoletto
20.1 IRLV by Wireless Channel Features 427
20.2 ML Classification for IRLV 428
20.3 Learning Phase Convergence 431
20.4 Experimental Results 433
20.5 Conclusions 437
Bibliography 437
21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439
M. Cenk Gursoy, Chen Zhong, and Senem Velipasalar
21.1 Introduction 439
21.2 System Model 441
21.3 Problem Formulation 443
21.4 Deep Actor-Critic Framework for Content Caching 446
21.5 Application to the Multi-Cell Network 448
21.6 Application to the Single-Cell Network with D2D Communications 452
21.7 Conclusion 454
Bibliography 455
Index 459