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2019029934 |
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9781119562313 |
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9781119562306 |
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9781119562276 |
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9781119562252 |
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eng. |
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pcc |
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TK5103.2 |
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| Classification number |
621.3840285/631 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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Luo, Fa-Long, |
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author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Machine learning for future wireless communications / |
| Statement of responsibility, etc |
Dr. Fa-Long Luo. |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Hoboken, NJ : |
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Wiley-IEEE, |
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2019. |
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1 online resource |
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text |
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computer |
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online resource |
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IEEE Press |
| 500 ## - GENERAL NOTE |
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| General note |
Includes index. |
| 500 ## - GENERAL NOTE |
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| General note |
FA-LONG LUO, Ph.D, Silicon Valley, California, USA<br/>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. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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Includes bibliographical references. |
| 505 ## - CONTENTS |
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| Formatted contents note |
TABLE OF CONTENTS<br/>List of Contributors xv<br/><br/>Preface xxi<br/><br/>Part I Spectrum Intelligence and Adaptive Resource Management 1<br/><br/>1 Machine Learning for Spectrum Access and Sharing 3<br/>Kobi Cohen<br/><br/>1.1 Introduction 3<br/><br/>1.2 Online Learning Algorithms for Opportunistic Spectrum Access 4<br/><br/>1.3 Learning Algorithms for Channel Allocation 9<br/><br/>1.4 Conclusions 19<br/><br/>Acknowledgments 20<br/><br/>Bibliography 20<br/><br/>2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 27<br/>Andres Kwasinski, Wenbo Wang, and Fatemeh Shah Mohammadi<br/><br/>2.1 Use of Q-Learning for Cross-layer Resource Allocation 29<br/><br/>2.2 Deep Q-Learning and Resource Allocation 33<br/><br/>2.3 Cooperative Learning and Resource Allocation 36<br/><br/>2.4 Conclusions 42<br/><br/>Bibliography 43<br/><br/>3 Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks 45<br/>Hadi Ghauch, Hossein Shokri-Ghadikolaei, Gabor Fodor, Carlo Fischione, and Mikael Skoglund<br/><br/>3.1 Background and Motivation 45<br/><br/>3.2 System Model and Problem Formulation 49<br/><br/>3.3 Hybrid Solution Approach 54<br/><br/>3.4 Conclusions and Discussions 59<br/><br/>Appendix A Appendix for Chapter 3 61<br/><br/>A.1 Overview of Reinforcement Learning 61<br/><br/>Bibliography 61<br/><br/>4 Deep Learning–Based Coverage and Capacity Optimization 63<br/>Andrei Marinescu, Zhiyuan Jiang, Sheng Zhou, Luiz A. DaSilva, and Zhisheng Niu<br/><br/>4.1 Introduction 63<br/><br/>4.2 Related Machine Learning Techniques for Autonomous Network Management 64<br/><br/>4.3 Data-Driven Base-Station Sleeping Operations by Deep Reinforcement Learning 67<br/><br/>4.4 Dynamic Frequency Reuse through a Multi-Agent Neural Network Approach 72<br/><br/>4.5 Conclusions 81<br/><br/>Bibliography 82<br/><br/>5 Machine Learning for Optimal Resource Allocation 85<br/>Marius Pesavento and Florian Bahlke<br/><br/>5.1 Introduction and Motivation 85<br/><br/>5.2 System Model 88<br/><br/>5.3 Resource Minimization Approaches 90<br/><br/>5.4 Numerical Results 96<br/><br/>5.5 Concluding Remarks 99<br/><br/>Bibliography 100<br/><br/>6 Machine Learning in Energy Efficiency Optimization 105<br/>Muhammad Ali Imran, Ana Flávia dos Reis, Glauber Brante, Paulo Valente Klaine, and Richard Demo Souza<br/><br/>6.1 Self-Organizing Wireless Networks 106<br/><br/>6.2 Traffic Prediction and Machine Learning 110<br/><br/>6.3 Cognitive Radio and Machine Learning 111<br/><br/>6.4 Future Trends and Challenges 112<br/><br/>6.5 Conclusions 114<br/><br/>Bibliography 114<br/><br/>7 Deep Learning Based Traffic and Mobility Prediction 119<br/>Honggang Zhang, Yuxiu Hua, Chujie Wang, Rongpeng Li, and Zhifeng Zhao<br/><br/>7.1 Introduction 119<br/><br/>7.2 Related Work 120<br/><br/>7.3 Mathematical Background 122<br/><br/>7.4 ANN-Based Models for Traffic and Mobility Prediction 124<br/><br/>7.5 Conclusion 133<br/><br/>Bibliography 134<br/><br/>8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing 137<br/>Benjamin Sliwa, Robert Falkenberg, and Christian Wietfeld<br/><br/>8.1 Mobile Crowdsensing 137<br/><br/>8.2 ML-Based Context-Aware Data Transmission 140<br/><br/>8.3 Methodology for Real-World Performance Evaluation 148<br/><br/>8.4 Results of the Real-World Performance Evaluation 149<br/><br/>8.5 Conclusion 152<br/><br/>Acknowledgments 154<br/><br/>Bibliography 154<br/><br/>Part II Transmission Intelligence and Adaptive Baseband Processing 157<br/><br/>9 Machine Learning–Based Adaptive Modulation and Coding Design 159<br/>Lin Zhang and Zhiqiang Wu<br/><br/>9.1 Introduction and Motivation 159<br/><br/>9.2 SL-Assisted AMC 162<br/><br/>9.3 RL-Assisted AMC 172<br/><br/>9.4 Further Discussion and Conclusions 178<br/><br/>Bibliography 178<br/><br/>10 Machine Learning–Based Nonlinear MIMO Detector 181<br/>Song-Nam Hong and Seonho Kim<br/><br/>10.1 Introduction 181<br/><br/>10.2 A Multihop MIMO Channel Model 182<br/><br/>10.3 Supervised-Learning-based MIMO Detector 184<br/><br/>10.4 Low-Complexity SL (LCSL) Detector 188<br/><br/>10.5 Numerical Results 191<br/><br/>10.6 Conclusions 193<br/><br/>Bibliography 193<br/><br/>11 Adaptive Learning for Symbol Detection: A Reproducing Kernel Hilbert Space Approach 197<br/>Daniyal Amir Awan, Renato Luis Garrido Cavalcante, Masahario Yukawa, and Slawomir Stanczak<br/><br/>11.1 Introduction 197<br/><br/>11.2 Preliminaries 198<br/><br/>11.3 System Model 200<br/><br/>11.4 The Proposed Learning Algorithm 203<br/><br/>11.5 Simulation 207<br/><br/>11.6 Conclusion 208<br/><br/>Appendix A Derivation of the Sparsification Metric and the Projections onto the Subspace Spanned by the Nonlinear Dictionary 210<br/><br/>Bibliography 211<br/><br/>12 Machine Learning for Joint Channel Equalization and Signal Detection 213<br/>Lin Zhang and Lie-Liang Yang<br/><br/>12.1 Introduction 213<br/><br/>12.2 Overview of Neural Network-Based Channel Equalization 214<br/><br/>12.3 Principles of Equalization and Detection 219<br/><br/>12.5 Performance of OFDM Systems With Neural Network-Based Equalization 232<br/><br/>12.6 Conclusions and Discussion 236<br/><br/>Bibliography 237<br/><br/>13 Neural Networks for Signal Intelligence: Theory and Practice 243<br/>Jithin Jagannath, Nicholas Polosky, Anu Jagannath, Francesco Restuccia, and Tommaso Melodia<br/><br/>13.1 Introduction 243<br/><br/>13.2 Overview of Artificial Neural Networks 244<br/><br/>13.3 Neural Networks for Signal Intelligence 248<br/><br/>13.4 Neural Networks for Spectrum Sensing 255<br/><br/>13.5 Open Problems 259<br/><br/>13.6 Conclusion 260<br/><br/>Bibliography 260<br/><br/>14 Channel Coding with Deep Learning: An Overview 265<br/>Shugong Xu<br/><br/>14.1 Overview of Channel Coding and Deep Learning 265<br/><br/>14.2 DNNs for Channel Coding 268<br/><br/>14.3 CNNs for Decoding 277<br/><br/>14.4 RNNs for Decoding 279<br/><br/>14.5 Conclusions 283<br/><br/>Bibliography 283<br/><br/>15 Deep Learning Techniques for Decoding Polar Codes 287<br/>Warren J. Gross, Nghia Doan, Elie Ngomseu Mambou, and Seyyed Ali Hashemi<br/><br/>15.1 Motivation and Background 287<br/><br/>15.2 Decoding of Polar Codes: An Overview 289<br/><br/>15.3 DL-Based Decoding for Polar Codes 292<br/><br/>15.4 Conclusions 299<br/><br/>Bibliography 299<br/><br/>16 Neural Network–Based Wireless Channel Prediction 303<br/>Wei Jiang, Hans Dieter Schotten, and Ji-ying Xiang<br/><br/>16.1 Introduction 303<br/><br/>16.2 Adaptive Transmission Systems 305<br/><br/>16.3 The Impact of Outdated CSI 307<br/><br/>16.4 Classical Channel Prediction 309<br/><br/>16.5 NN-Based Prediction Schemes 313<br/><br/>16.6 Summary 323<br/><br/>Bibliography 323<br/><br/>Part III Network Intelligence and Adaptive System Optimization 327<br/><br/>17 Machine Learning for Digital Front-End: a Comprehensive Overview 329<br/>Pere L. Gilabert, David López-Bueno, Thi Quynh Anh Pham, and Gabriel Montoro<br/><br/>17.1 Motivation and Background 329<br/><br/>17.2 Overview of CFR and DPD 331<br/><br/>17.3 Dimensionality Reduction and ML 341<br/><br/>17.4 Nonlinear Neural Network Approaches 350<br/><br/>17.5 Support Vector Regression Approaches 368<br/><br/>17.6 Further Discussion and Conclusions 373<br/><br/>Bibliography 374<br/><br/>18 Neural Networks for Full-Duplex Radios: Self-Interference Cancellation 383<br/>Alexios Balatsoukas-Stimming<br/><br/>18.1 Nonlinear Self-Interference Models 384<br/><br/>18.2 Digital Self-Interference Cancellation 386<br/><br/>18.3 Experimental Results 391<br/><br/>18.4 Conclusions 393<br/><br/>Bibliography 395<br/><br/>19 Machine Learning for Context-Aware Cross-Layer Optimization 397<br/>Yang Yang, Zening Liu, Shuang Zhao, Ziyu Shao, and Kunlun Wang<br/><br/>19.1 Introduction 397<br/><br/>19.2 System Model 399<br/><br/>19.3 Problem Formulation and Analytical Framework 402<br/><br/>19.4 Predictive Multi-tier Operations Scheduling (PMOS) Algorithm 409<br/><br/>19.5 A Multi-tier Cost Model for User Scheduling in Fog Computing Networks 413<br/><br/>19.6 Conclusion 420<br/><br/>Bibliography 421<br/><br/>20 Physical-Layer Location Verification by Machine Learning 425<br/>Stefano Tomasin, Alessandro Brighente, Francesco Formaggio, and Gabriele Ruvoletto<br/><br/>20.1 IRLV by Wireless Channel Features 427<br/><br/>20.2 ML Classification for IRLV 428<br/><br/>20.3 Learning Phase Convergence 431<br/><br/>20.4 Experimental Results 433<br/><br/>20.5 Conclusions 437<br/><br/>Bibliography 437<br/><br/>21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439<br/>M. Cenk Gursoy, Chen Zhong, and Senem Velipasalar<br/><br/>21.1 Introduction 439<br/><br/>21.2 System Model 441<br/><br/>21.3 Problem Formulation 443<br/><br/>21.4 Deep Actor-Critic Framework for Content Caching 446<br/><br/>21.5 Application to the Multi-Cell Network 448<br/><br/>21.6 Application to the Single-Cell Network with D2D Communications 452<br/><br/>21.7 Conclusion 454<br/><br/>Bibliography 455<br/><br/>Index 459 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
"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"-- |
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Provided by publisher. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
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| Source of description note |
Description based on print version record and CIP data provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Wireless communication systems. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Neural networks (Computer science) |
| 655 #0 - INDEX TERM--GENRE/FORM |
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Electronic books. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
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| Link text |
Full text available at Wiley Online Library Click here to view |
| Uniform Resource Identifier |
https://onlinelibrary.wiley.com/doi/book/10.1002/9781119562306 |
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