Machine Learning for Future Wireless Communications

A comprehensive review of 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 of the theory, applications, and current research developments 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 address the big challenges in wireless communications and networks, such as 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 subjects 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 adaptation, radio access control, smart proactive caching, and adaptive resource allocations.

Uniquely organized into three categories—Spectrum Intelligence, Transmission Intelligence, and Network Intelligence—this essential 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.