Resource Optimization In Wireless Communications

Resource Optimization in Wireless Communications

Resource Optimization in Wireless Communications: Fundamentals, Algorithms, and Applications provides an easy-to-understand overview of the fundamentals of resource optimization, along with the latest algorithms and applications for emerging 5G, and beyond, wireless systems offering a variety of services. Additionally, it covers the principles and resource optimization of some systems expected in 6G.This book is suitable for courses in wireless communications that cover the principles of multicarrier and OFDM, the theory of resource allocation, power allocation, and subcarrier allocation, as well as the principles and optimization of OTFS, ISAC, reflective intelligent surface (RIS)-assisted mmWave, and user-centric cell-free wireless systems. It is also an ideal self-study reference text for researchers and industry engineers who wish to deepen their knowledge while researching and developing wireless systems for 6G. - Provides a comprehensive introduction to resource optimization in wireless communications, laying a strong foundation for researchers developing cutting-edge resource-allocation algorithms. - Includes a wide variety of resource-optimization algorithms that are ready for direct application in both research and design. - Accompanied by practical examples to enhance understanding, making it ideal for self-study and hands-on practice. - Explores resource optimization across a broad spectrum of 5G/6G wireless systems. - Features numerous illustrations that effectively demonstrate the performance capabilities of various resource-allocation algorithms.

Fundamentals of Resource Allocation in Wireless Networks

Machine Learning for Future Wireless Communications

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 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.