Optimal Resource Allocation in Coordinated Multi-Cell Systems provides a solid grounding and understanding for optimization of practical multi-cell systems and will be of interest to all researchers and engineers working on the practical design of such systems.

This reference text discusses advances in wireless communication, design challenges, and future research directions to design reliable wireless communication. The text discusses emerging technologies including wireless sensor networks, Internet of Things (IoT), cloud computing, mm-Wave, Massive MIMO, cognitive radios (CR), visible light communication (VLC), wireless optical communication, signal processing, and channel modeling. The text covers artificial intelligence-based applications in wireless communication, machine learning techniques and challenges in wireless sensor networks, and deep learning for channel and bandwidth estimation during optical wireless communication. The text will be useful for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, and electronics and communication engineering.

This SpringerBrief discusses the current research on coordinated multipoint transmission/reception (CoMP) in wireless multi-cell systems. This book analyzes the structure of the CoMP precoders and the message exchange mechanism in the CoMP system in order to reveal the advantage of CoMP. Topics include interference management in wireless cellular networks, joint signal processing, interference coordination, uplink and downlink precoding and system models. After an exploration of the motivations and concepts of CoMP, the authors present the architectures of a CoMP system. Practical implementation and operational challenges of CoMP are discussed in detail. Also included is a review of CoMP architectures and deployment scenarios in the LTE-Advanced standard. Readers are exposed to the latest multiuser precoding designs for the CoMP system under two operating modes, interference aware and interference coordination. Wireless Coordinated Multi cell Systems: Architectures and Precoding Designs is a concise and approachable tool for researchers, professionals and advanced-level students interested in wireless communications and networks.

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.

Wireless communications have a strong impact on improving the quality of life in this century. Smart phones industry is now considered one of the most attractive fields, so advanced research is conducted in order to improve the quality of service in wireless communication environments. Many design challenges such as power consumption, quality of service, low cost, high data rate and small size are being treated every day. This book aims to provide highlights of the current research in the field of wireless communications. The subjects discussed are very valuable to communication researchers as well as researchers in the wireless related areas. The book chapters cover a wide range of wireless communication topics that are considered key technologies for future applications.

The first book on Cloud Radio Access Networks (C-RANs), covering fundamental theory, current techniques, and potential applications.

This book presents a unified framework for the tractable analysis of large-scale, multi-antenna wireless networks using stochastic geometry. This mathematical analysis is essential for assessing and understanding the performance of complicated multi-antenna networks, which are one of the foundations of 5G and beyond networks to meet the ever-increasing demands for network capacity. Describing the salient properties of the framework, which makes the analysis of multi-antenna networks comparable to that of their single-antenna counterparts, the book discusses effective design approaches that do not require complex system-level simulations. It also includes various application examples with different multi-antenna network models to illustrate the framework’s effectiveness.