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Informationen zum Autor 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. Inhaltsverzeichnis 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 ...
