Machine learning for future wireless communications / Dr. Fa-Long Luo.

Includes index.

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.

Includes bibliographical references.

TABLE OF CONTENTS
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 Flávia 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 López-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

"Due to its powerful nonlinear mapping and distribution processing capability, deep neural networks based machine learning technology is being considered as a very promising tool to attack the big challenge in wireless communications and networks imposed by the explosively increasing demands in terms of capacity, coverage, latency, efficiency (power, frequency spectrum and other resources), flexibility, compatibility, quality of experience and silicon convergence. Mainly categorized into the supervised learning, the unsupervised learning and the reinforcement learning, various machine learning algorithms can be used to provide a better channel modelling and estimation in millimeter and terahertz bands, to select a more adaptive modulation (waveform, coding rate, bandwidth, and filtering structure) in massive multiple-input and multiple-output (MIMO) technology, to design a more efficient front-end and radio-frequency processing (pre-distortion for power amplifier compensation, beamforming configuration and crest-factor reduction), to deliver a better compromise in self-interference cancellation for full-duplex transmissions and device-to-device communications, and to offer a more practical solution for intelligent network optimization, mobile edge computing, networking slicing and radio resource management related to wireless big data, mission critical communications, massive machine-type communications and tactile internet"-- Provided by publisher.

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