Machine learning for future wireless communications / edited by Fa-Long Luo.

Includes bibliographical references and index.

Part I Spectrum intelligence and adaptive resource management -- Machine learning for spectrum access and sharing -- Reinforcement learning for resource allocation in cognitive radio networks -- Machine learning for spectrum sharing in millimeter-wave cellular -- Deep learning-based coverage and capacity optimization -- Machine learning for optimal resource allocation -- Machine learning in energy efficiency optimization -- Deep learning based traffic and mobility prediction -- Machine learning for resource-efficient data transfer in mobile crowdsensing -- Part II Transmission intelligence and adaptive baseband processing -- Machine learning-based adaptive modulation and coding design -- Machine learning-based nonlinear MIMO detector -- Adaptive learning for symbol detection: a reproducing Kernel Hilbert space approach -- Machine learning for joint channel equalization and signal detection -- Neural networks for signal intelligence: theory and practice -- Channel coding with deep learning: an overview -- deep learning techniques for decoding polar codes -- Neural network-based wireless channel prediction -- Part III Network intelligence and adaptive system optimization -- Machine learning for digital front-end: a comprehensive overview -- Neural networks for full-duplex radios: self-interference cancellation -- Machine learning for context-aware cross layer optimization -- Physical-layer location verification by machine learning -- Deep multi-agent reinforcement learning for cooperative edge caching.

"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"-- Provided by publisher.