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Machine Learning for Future Wireless Communications


Machine Learning for Future Wireless Communications


1. Aufl.

von: Fa-Long Luo

113,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 13.12.2019
ISBN/EAN: 9781119562276
Sprache: englisch
Anzahl Seiten: 496

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Beschreibungen

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.  
List of Contributors xv Preface xxi Part I Spectrum Intelligence and Adaptive Resource Management 1 1 Machine Learning for Spectrum Access and Sharing 3Kobi Cohen 1.1 Introduction 3 1.2 Online Learning Algorithms for Opportunistic Spectrum Access 4 1.2.1 The Network Model 4 1.2.2 Performance Measures of the Online Learning Algorithms 5 1.2.3 The Objective 6 1.2.4 Random and Deterministic Approaches 6 1.2.5 The Adaptive Sequencing Rules Approach 7 1.2.5.1 Structure of Transmission Epochs 7 1.2.5.2 Selection Rule under the ASR Algorithm 8 1.2.5.3 High-Level Pseudocode and Implementation Discussion 9 1.3 Learning Algorithms for Channel Allocation 9 1.3.1 The Network Model 10 1.3.2 Distributed Learning, Game-Theoretic, and Matching Approaches 11 1.3.3 Deep Reinforcement Learning for DSA 13 1.3.3.1 Background on Q-learning and Deep Reinforcement Learning (DRL): 13 1.3.4 Existing DRL-Based Methods for DSA 14 1.3.5 Deep Q-Learning for Spectrum Access (DQSA) Algorithm 15 1.3.5.1 Architecture of the DQN Used in the DQSA Algorithm 15 1.3.5.2 Training the DQN and Online Spectrum Access 16 1.3.5.3 Simulation Results 17 1.4 Conclusions 19 Acknowledgments 20 Bibliography 20 2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 27Andres 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 45Hadi Ghauch, Hossein Shokri-Ghadikolaei, Gabor Fodor, Carlo Fischione, and Mikael Skoglund 3.1 Background and Motivation 45 3.1.1 Review of Cellular Network Evolution 45 3.1.2 Millimeter-Wave and Large-Scale Antenna Systems 46 3.1.3 Review of Spectrum Sharing 47 3.1.4 Model-Based vs. Data-Driven Approaches 48 3.2 System Model and Problem Formulation 49 3.2.1 Models 49 3.2.1.1 Network Model 49 3.2.1.2 Association Model 49 3.2.1.3 Antenna and Channel Model 49 3.2.1.4 Beamforming and Coordination Models 50 3.2.1.5 Coordination Model 50 3.2.2 Problem Formulation 51 3.2.2.1 Rate Models 52 3.2.3 Model-based Approach 52 3.2.4 Data-driven Approach 53 3.3 Hybrid Solution Approach 54 3.3.1 Data-Driven Component 55 3.3.2 Model-Based Component 56 3.3.2.1 Illustrative Numerical Results 58 3.3.3 Practical Considerations 58 3.3.3.1 Implementing Training Frames 58 3.3.3.2 Initializations 59 3.3.3.3 Choice of the Penalty Matrix 59 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 63Andrei 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 4.2.1 Reinforcement Learning and Neural Networks 64 4.2.2 Application to Mobile Networks 66 4.3 Data-Driven Base-Station Sleeping Operations by Deep Reinforcement Learning 67 4.3.1 Deep Reinforcement Learning Architecture 67 4.3.2 Deep Q-Learning Preliminary 68 4.3.3 Applications to BS Sleeping Control 68 4.3.3.1 Action-Wise Experience Replay 69 4.3.3.2 Adaptive Reward Scaling 70 4.3.3.3 Environment Models and Dyna Integration 70 4.3.3.4 DeepNap Algorithm Description 71 4.3.4 Experiments 71 4.3.4.1 Algorithm Comparisons 71 4.3.5 Summary 72 4.4 Dynamic Frequency Reuse through a Multi-Agent Neural Network Approach 72 4.4.1 Multi-Agent System Architecture 73 4.4.1.1 Cell Agent Architecture 75 4.4.2 Application to Fractional Frequency Reuse 75 4.4.3 Scenario Implementation 76 4.4.3.1 Cell Agent Neural Network 76 4.4.4 Evaluation 78 4.4.4.1 Neural Network Performance 78 4.4.4.2 Multi-Agent System Performance 79 4.4.5 Summary 81 4.5 Conclusions 81 Bibliography 82 5 Machine Learning for Optimal Resource Allocation 85Marius Pesavento and Florian Bahlke 5.1 Introduction and Motivation 85 5.1.1 Network Capacity and Densification 86 5.1.2 Decentralized Resource Minimization 87 5.1.3 Overview 88 5.2 System Model 88 5.2.1 Heterogeneous Wireless Networks 88 5.2.2 Load Balancing 89 5.3 Resource Minimization Approaches 90 5.3.1 Optimized Allocation 91 5.3.2 Feature Selection and Training 91 5.3.3 Range Expansion Optimization 93 5.3.4 Range Expansion Classifier Training 94 5.3.5 Multi-Class Classification 94 5.4 Numerical Results 96 5.5 Concluding Remarks 99 Bibliography 100 6 Machine Learning in Energy Efficiency Optimization 105Muhammad 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 6.4.1 Deep Learning 112 6.4.2 Positioning of Unmanned Aerial Vehicles 113 6.4.3 Learn-to-Optimize Approaches 113 6.4.4 Some Challenges 114 6.5 Conclusions 114 Bibliography 114 7 Deep Learning Based Traffic and Mobility Prediction 119Honggang Zhang, Yuxiu Hua, Chujie Wang, Rongpeng Li, and Zhifeng Zhao 7.1 Introduction 119 7.2 Related Work 120 7.2.1 Traffic Prediction 120 7.2.2 Mobility Prediction 121 7.3 Mathematical Background 122 7.4 ANN-Based Models for Traffic and Mobility Prediction 124 7.4.1 ANN for Traffic Prediction 124 7.4.1.1 Long Short-Term Memory Network Solution 124 7.4.1.2 Random Connectivity Long Short-Term Memory Network Solution 125 7.4.2 ANN for Mobility Prediction 128 7.4.2.1 Basic LSTM Network for Mobility Prediction 128 7.4.2.2 Spatial-Information-Assisted LSTM-Based Framework of Individual Mobility Prediction 130 7.4.2.3 Spatial-Information-Assisted LSTM-Based Framework of Group Mobility Prediction 131 7.5 Conclusion 133 Bibliography 134 8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing 137Benjamin Sliwa, Robert Falkenberg, and Christian Wietfeld 8.1 Mobile Crowdsensing 137 8.1.1 Applications and Requirements 138 8.1.2 Anticipatory Data Transmission 139 8.2 ML-Based Context-Aware Data Transmission 140 8.2.1 Groundwork: Channel-aware Transmission 140 8.2.2 Groundwork: Predictive CAT 142 8.2.3 ML-based CAT 144 8.2.4 ML-based pCAT 146 8.3 Methodology for Real-World Performance Evaluation 148 8.3.1 Evaluation Scenario 148 8.3.2 Power Consumption Analysis 148 8.4 Results of the Real-World Performance Evaluation 149 8.4.1 Statistical Properties of the Network Quality Indicators 149 8.4.2 Comparison of the Transmission Schemes 149 8.4.3 Summary 151 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 159Lin Zhang and Zhiqiang Wu 9.1 Introduction and Motivation 159 9.1.1 Overview of ML-Assisted AMC 160 9.1.2 MCS Schemes Specified by IEEE 802.11n 161 9.2 SL-Assisted AMC 162 9.2.1 k-NN-Assisted AMC 162 9.2.1.1 Algorithm for k-NN-Assisted AMC 163 9.2.2 Performance Analysis of k-NN-Assisted AMC System 164 9.2.3 SVM-Assisted AMC 166 9.2.3.1 SVM Algorithm 166 9.2.3.2 Simulation and Results 170 9.3 RL-Assisted AMC 172 9.3.1 Markov Decision Process 172 9.3.2 Solution for the Markov Decision 173 9.3.3 Actions, States, and Rewards 174 9.3.4 Performance Analysis and Simulations 175 9.4 Further Discussion and Conclusions 178 Bibliography 178 10 Machine Learning–Based Nonlinear MIMO Detector 181Song-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 10.3.1 Non-Parametric Learning 184 10.3.2 Parametric Learning 185 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 197Daniyal Amir Awan, Renato Luis Garrido Cavalcante, Masahario Yukawa, and Slawomir Stanczak 11.1 Introduction 197 11.2 Preliminaries 198 11.2.1 Reproducing Kernel Hilbert Spaces 198 11.2.2 Sum Spaces of Reproducing Kernel Hilbert Spaces 199 11.3 System Model 200 11.3.1 Symbol Detection in Multiuser Environments 201 11.3.2 Detection of Complex-Valued Symbols in Real Hilbert Spaces 202 11.4 The Proposed Learning Algorithm 203 11.4.1 The Canonical Iteration 203 11.4.2 Practical Issues 204 11.4.3 Online Dictionary Learning 205 11.4.3.1 Dictionary for the Linear Component 206 11.4.3.2 Dictionary for the Gaussian Component 206 11.4.4 The Online Learning Algorithm 206 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 213Lin Zhang and Lie-Liang Yang 12.1 Introduction 213 12.2 Overview of Neural Network-Based Channel Equalization 214 12.2.1 Multilayer Perceptron-Based Equalizers 215 12.2.2 Functional Link Artificial Neutral Network-Based Equalizers 215 12.2.3 Radial Basis Function-Based Equalizers 216 12.2.4 Recurrent Neural Networks-Based Equalizers 216 12.2.5 Self-Constructing Recurrent Fuzzy Neural Network-Based Equalizers 217 12.2.6 Deep-Learning-Based Equalizers 217 12.2.7 Extreme Learning Machine–Based Equalizers 218 12.2.8 SVM- and GPR-Based Equalizers 218 12.3 Principles of Equalization and Detection 219 12.4 NN-Based Equalization and Detection 223 12.4.1 Multilayer Perceptron Model 223 12.4.1.1 Generalized Multilayer Perceptron Structure 224 12.4.1.2 Gradient Descent Algorithm 225 12.4.1.3 Forward and Backward Propagation 226 12.4.2 Deep-Learning Neural Network-Based Equalizers 227 12.4.2.1 System Model and Network Structure 227 12.4.2.2 Network Training 228 12.4.3 Convolutional Neural Network-Based Equalizers 229 12.4.4 Recurrent Neural Network-Based Equalizers 231 12.5 Performance of OFDM Systems With Neural Network-Based Equalization 232 12.5.1 System Model and Network Structure 232 12.5.2 DNN and CNN Network Structure 233 12.5.3 Offline Training and Online Deployment 234 12.5.4 Simulation Results and Analyses 235 12.6 Conclusions and Discussion 236 Bibliography 237 13 Neural Networks for Signal Intelligence: Theory and Practice 243Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, and Tommaso Melodia 13.1 Introduction 243 13.2 Overview of Artificial Neural Networks 244 13.2.1 Feedforward Neural Networks 244 13.2.2 Convolutional Neural Networks 247 13.3 Neural Networks for Signal Intelligence 248 13.3.1 Modulation Classification 249 13.3.2 Wireless Interference Classification 252 13.4 Neural Networks for Spectrum Sensing 255 13.4.1 Existing Work 256 13.4.2 Background on System-on-Chip Computer Architecture 256 13.4.3 A Design Framework for Real-Time RF Deep Learning 257 13.4.3.1 High-Level Synthesis 257 13.4.3.2 Design Steps 258 13.5 Open Problems 259 13.5.1 Lack of Large-Scale Wireless Signal Datasets 259 13.5.2 Choice of I/Q Data Representation Format 259 13.5.3 Choice of Learning Model and Architecture 260 13.6 Conclusion 260 Bibliography 260 14 Channel Coding with Deep Learning: An Overview 265Shugong Xu 14.1 Overview of Channel Coding and Deep Learning 265 14.1.1 Channel Coding 265 14.1.2 Deep Learning 266 14.2 DNNs for Channel Coding 268 14.2.1 Using DNNs to Decode Directly 269 14.2.2 Scaling DL Method 271 14.2.3 DNNs for Joint Equalization and Channel Decoding 272 14.2.4 A Unified Method to Decode Multiple Codes 274 14.2.5 Summary 276 14.3 CNNs for Decoding 277 14.3.1 Decoding by Eliminating Correlated Channel Noise 277 14.3.1.1 BP-CNN Reduces Decoding BER 279 14.3.1.2 Multiple Iterations Between CNN and BP Further Improve Performance 279 14.3.2 Summary 279 14.4 RNNs for Decoding 279 14.4.1 Using RNNs to Decode Sequential Codes 279 14.4.2 Improving the Standard BP Algorithm with RNNs 281 14.4.3 Summary 283 14.5 Conclusions 283 Bibliography 283 15 Deep Learning Techniques for Decoding Polar Codes 287Warren 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 15.2.1 Problem Formulation of Polar Codes 289 15.2.2 Successive-Cancellation Decoding 290 15.2.3 Successive-Cancellation List Decoding 291 15.2.4 Belief Propagation Decoding 291 15.3 DL-Based Decoding for Polar Codes 292 15.3.1 Off-the-Shelf DL Decoders for Polar Codes 292 15.3.2 DL-Aided Decoders for Polar Codes 293 15.3.2.1 Neural Belief Propagation Decoders 293 15.3.2.2 Joint Decoder and Noise Estimator 295 15.3.3 Evaluation 296 15.4 Conclusions 299 Bibliography 299 16 Neural Network–Based Wireless Channel Prediction 303Wei Jiang, Hans Dieter Schotten, and Ji-ying Xiang 16.1 Introduction 303 16.2 Adaptive Transmission Systems 305 16.2.1 Transmit Antenna Selection 305 16.2.2 Opportunistic Relaying 306 16.3 The Impact of Outdated CSI 307 16.3.1 Modeling Outdated CSI 307 16.3.2 Performance Impact 308 16.4 Classical Channel Prediction 309 16.4.1 Autoregressive Models 310 16.4.2 Parametric Models 311 16.5 NN-Based Prediction Schemes 313 16.5.1 The RNN Architecture 313 16.5.2 Flat-Fading SISO Prediction 314 16.5.2.1 Channel Gain Prediction with a Complex-Valued RNN 314 16.5.2.2 Channel Gain Prediction with a Real-Valued RNN 315 16.5.2.3 Channel Envelope Prediction 315 16.5.2.4 Multi-Step Prediction 316 16.5.3 Flat-Fading MIMO Prediction 316 16.5.3.1 Channel Gain Prediction 317 16.5.3.2 Channel Envelope Prediction 317 16.5.4 Frequency-Selective MIMO Prediction 317 16.5.5 Prediction-Assisted MIMO-OFDM 319 16.5.6 Performance and Complexity 320 16.5.6.1 Computational Complexity 320 16.5.6.2 Performance 321 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 329Pere 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 17.2.1 Crest Factor Reduction Techniques 331 17.2.2 Power Amplifier Behavioral Modeling 334 17.2.3 Closed-Loop Digital Predistortion Linearization 335 17.2.4 Regularization 337 17.2.4.1 Ridge Regression or Tikhonov ??2 Regularization 338 17.2.4.2 LASSO or ??1 Regularization 339 17.2.4.3 Elastic Net 340 17.3 Dimensionality Reduction and ML 341 17.3.1 Introduction 341 17.3.2 Dimensionality Reduction Applied to DPD Linearization 343 17.3.3 Greedy Feature-Selection Algorithm: OMP 345 17.3.4 Principal Component Analysis 345 17.3.5 Partial Least Squares 348 17.4 Nonlinear Neural Network Approaches 350 17.4.1 Introduction to ANN Topologies 350 17.4.2 Design Considerations for Digital Linearization and RF Impairment Correction 353 17.4.2.1 ANN Architectures for Single-Antenna DPD 354 17.4.2.2 ANN Architectures for MIMO DPD, I/Q Imbalances, and DC Offset Correction 355 17.4.2.3 ANN Training and Parameter Extraction Procedure 357 17.4.2.4 Validation Methodologies and Key Performance Index 361 17.4.3 ANN for CFR: Design and Key Performance Index 364 17.4.3.1 SLM and PTS 364 17.4.3.2 Tone Injection 365 17.4.3.3 ACE 366 17.4.3.4 Clipping and Filtering 368 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 383Alexios Balatsoukas-Stimming 18.1 Nonlinear Self-Interference Models 384 18.1.1 Nonlinear Self-Interference Model 385 18.2 Digital Self-Interference Cancellation 386 18.2.1 Linear Cancellation 386 18.2.2 Polynomial Nonlinear Cancellation 387 18.2.3 Neural Network Nonlinear Cancellation 387 18.2.4 Computational Complexity 389 18.2.4.1 Linear Cancellation 389 18.2.4.2 Polynomial Nonlinear Cancellation 390 18.2.4.3 Neural Network Nonlinear Cancellation 390 18.3 Experimental Results 391 18.3.1 Experimental Setup 391 18.3.2 Self-Interference Cancellation Results 391 18.3.3 Computational Complexity 392 18.4 Conclusions 393 18.4.1 Open Problems 394 Bibliography 395 19 Machine Learning for Context-Aware Cross-Layer Optimization 397Yang 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 19.3.1 Fog-Enabled Multi-Tier Operations Scheduling (FEMOS) Algorithm 403 19.3.2 Theoretical and Numerical Analysis 405 19.3.2.1 Theoretical Analysis 405 19.3.2.2 Numerical Analysis 406 19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm 409 19.4.1 System Model 409 19.4.2 Theoretical Analysis 411 19.4.3 Numerical Analysis 413 19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks 413 19.5.1 System Model and Problem Formulation 413 19.5.2 COUS Algorithm 416 19.5.3 Performance Evaluation 418 19.6 Conclusion 420 Bibliography 421 20 Physical-Layer Location Verification by Machine Learning 425Stefano Tomasin, Alessandro Brighente, Francesco Formaggio, and Gabriele Ruvoletto 20.1 IRLV by Wireless Channel Features 427 20.1.1 Optimal Test 428 20.2 ML Classification for IRLV 428 20.2.1 Neural Networks 429 20.2.2 Support Vector Machines 430 20.2.3 ML Classification Optimality 431 20.3 Learning Phase Convergence 431 20.3.1 Fundamental Learning Theorem 431 20.3.2 Simulation Results 432 20.4 Experimental Results 433 20.5 Conclusions 437 Bibliography 437 21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439M. Cenk Gursoy, Chen Zhong, and Senem Velipasalar 21.1 Introduction 439 21.2 System Model 441 21.2.1 Multi-Cell Network Model 441 21.2.2 Single-Cell Network Model with D2D Communication 442 21.2.3 Action Space 443 21.3 Problem Formulation 443 21.3.1 Cache Hit Rate 443 21.3.2 Transmission Delay 444 21.4 Deep Actor-Critic Framework for Content Caching 446 21.5 Application to the Multi-Cell Network 448 21.5.1 Experimental Settings 448 21.5.2 Simulation Setup 448 21.5.3 Simulation Results 449 21.5.3.1 Cache Hit Rate 449 21.5.3.2 Transmission Delay 450 21.5.3.3 Time-Varying Scenario 451 21.6 Application to the Single-Cell Network with D2D Communications 452 21.6.1 Experimental Settings 452 21.6.2 Simulation Setup 452 21.6.3 Simulation Results 453 21.6.3.1 Cache Hit Rate 453 21.6.3.2 Transmission Delay 454 21.7 Conclusion 454 Bibliography 455 Index 459
FA-LONG LUO, Ph.D, Silicon Valley, California, USADr. 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.
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 all 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.

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