Machine Learning for Future Wireless Communications: (IEEE Press)

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.  

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