Название: Machine Learning for Future Wireless Communications Автор: Fa-Long Luo Издательство: Wiley-IEEE Press Год: 2020 Страниц: 475 Язык: английский Формат: pdf (true) Размер: 20.9 MB
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 (ML) 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.
Due to its powerful nonlinear mapping and distribution processing capability, deep NN-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, e?ciency (power, frequency spectrum, and other resources), ?exibility, compatibility, quality of experience, and silicon convergence.
Mainly categorized into supervised learning, unsupervised learning, and reinforcement learning, various machine learning (ML) algorithms can be used to provide better channel modeling and estimation in millimeter and terahertz bands; to select a more adaptive modulation (waveform, coding rate, bandwidth, and ?ltering structure) in massive multiple-input and multiple-output (MIMO) technology; to design more e?cient front-end and radio-frequency processing (pre-distortion for power ampli?er compensation, beamforming con?guration, and crest-factor reduction); to deliver a better compromise in self-interference cancellation for full-duplex transmissions and device-to-device communications; and to o?er 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.
List of Contributors xv Preface xxi Part I Spectrum Intelligence and Adaptive Resource Management 1 1 Machine Learning for Spectrum Access and Sharing 3 2 Reinforcement Learning for Resource Allocation in Cognitive Radio Networks 27 3 Machine Learning for Spectrum Sharing in Millimeter-Wave Cellular Networks 45 Appendix A Appendix for Chapter 3 61 A.1 Overview of Reinforcement Learning 61 4 Deep Learning–Based Coverage and Capacity Optimization 63 5 Machine Learning for Optimal Resource Allocation 85 6 Machine Learning in Energy Efficiency Optimization 105 7 Deep Learning Based Traffic and Mobility Prediction 119 8 Machine Learning for Resource-Efficient Data Transfer in Mobile Crowdsensing 137 Part II Transmission Intelligence and Adaptive Baseband Processing 157 9 Machine Learning–Based Adaptive Modulation and Coding Design 159 10 Machine Learning–Based Nonlinear MIMO Detector 181 11 Adaptive Learning for Symbol Detection: A Reproducing Kernel Hilbert Space Approach 197 12 Machine Learning for Joint Channel Equalization and Signal Detection 213 13 Neural Networks for Signal Intelligence: Theory and Practice 243 ... 21 Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching 439
Скачать Machine Learning for Future Wireless Communications
Deep Reinforcement Learning for Wireless Networks Название: Deep Reinforcement Learning for Wireless Networks Автор: F. Richard Yu, Ying He Издательство: Springer Год: 2019 Язык: английский Формат:...
Навигация
Вход на сайт
Реклама
Machine Learning for Future Wireless Communications 
