This monograph presents a unified framework for energy efficiency maximization in wireless networks via fractional programming theory. The definition of energy efficiency is introduced, with reference to single-user and multi-user wireless networks, and it is observed how the problem of resource allocation for energy efficiency optimization is naturally cast as a fractional program. An extensive review of the state-of-the-art in energy efficiency optimization by fractional programming is provided, with reference to centralized and distributed resource allocation schemes. A solid background on fractional programming theory is provided. The key-notion of generalized concavity is presented and its strong connection with fractional functions described. A taxonomy of fractional problems is introduced, and for each class of fractional problem, general solution algorithms are described, discussing their complexity and convergence properties.

Energy Efficiency in Wireless Networks via Fractional Programming Theory provides a comprehensive introduction to the theoretical and practical aspects of energy-efficient wireless network design.

Understand the fundamental theory and practical design aspects of green and soft wireless communications networks with this expert text. It provides comprehensive and unified coverage of 5G physical layer design, as well as design of the higher and radio access layers and the core network, drawing on viewpoints from both academia and industry. Get to grips with the theory through authoritative discussion of information-theoretical results, and learn about fundamental green design trade-offs, software-defined network architectures, and energy efficient radio resource management strategies. Applications of wireless big data and artificial intelligence to wireless network design are included, providing an excellent design reference, and real-world examples of employment in software-defined 5G networks and energy saving solutions from wireless communications companies and cellular operators help to connect theory with practice. This is an essential text for graduate students, professionals and researchers.

This book introduces key modulation and predistortion techniques for approaching power and spectrum-efficient transmission for wireless communication systems. The book presents a combination of theoretical principles, practical implementations, and actual tests. It focuses on power and spectrally efficient modulation and transmission techniques in the portable wireless communication systems, and introduces currently developed and designed RF transceivers in the latest wireless markets. Most materials, design examples, and design strategies used are based on the author’s two decades of work in the digital communication fields, especially in the areas of the digital modulations, demodulations, digital signal processing, and linearization of power amplifiers. The applications of these practical products and equipment cover the satellite communications on earth station systems, microwave communication systems, 2G GSM and 3G WCDMA mobile communication systems, and 802.11 WLAN systems.>

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.

This book provides a broad understanding of the fundamental tools and methods from information theory and mathematical programming, as well as specific applications in 6G and beyond system designs. The contents focus on not only both theories but also their intersection in 6G. Motivations are from the multitude of new developments which will arise once 6G systems integrate new communication networks with AIoT (Artificial Intelligence plus Internet of Things). Design issues such as the intermittent connectivity, low latency, federated learning, IoT security, etc., are covered. This monograph provides a thorough picture of new results from information and optimization theories, as well as how their dialogues work to solve aforementioned 6G design issues.

This book constitutes the conference proceedings of the 10th International Conference on Network Games, Control and Optimization, NETGCOOP 2020, held in Cargèse, Corsica, France, in September 2021*.The 12 full papers and 16 short papers were carefully reviewed and selected from 44 submissions. The papers are organized in the following topical sections: ​game theory and iterative algorithms applied to wireless communication; stochastic models for network performance analysis; game theory in mobile and wireless networks; scheduling and resource allocation problems in networks; advance in game theory; social network; electrical network. * The conference was postponed to 2021 due to the COVID-19 pandemic.