F. Richard Yu

With rapid population explosion, improving rail transit speed and capacity is strongly desirable around the world. Communication-based train control (CBTC) is an automated train control system using high capacity bidirectional train-ground communications to ensure the safe operation of rail vehicles. This book presents the latest advances in CBTC r

Advances in Communications-Based Train Control Systems

Cognitive radios (CR) technology is capable of sensing its surrounding environment and adapting its internal states by making corresponding changes in certain operating parameters. CR is envisaged to solve the problems of the limited available spectrum and the inefficiency in the spectrum usage. CR has been considered in mobile ad hoc networks (MANETs), which enable wireless devices to dynamically establish networks without necessarily using a fixed infrastructure. The changing spectrum environment and the importance of protecting the transmission of the licensed users of the spectrum mainly differentiate classical MANETs from CR-MANETs. The cognitive capability and re-configurability of CR-MANETs have opened up several areas of research which have been explored extensively and continue to attract research and development. The book will describe CR-MANETs concepts, intrinsic properties and research challenges of CR-MANETs. Distributed spectrum management functionalities, such as spectrum sensing and sharing, will be presented. The design, optimization and performance evaluation of security issues and upper layers in CR-MANETs, such as transport and application layers, will be investigated.

Cognitive Radio Mobile Ad Hoc Networks

Victor C.M. Leung

Xi Zhang

Green Communications and Networking introduces novel solutions that can bring about significant reductions in energy consumption in the information and communication technology (ICT) industry-as well as other industries, including electric power. Containing the contributions of leading experts in the field, it examines the latest research advances

Green Communications and Networking

Ying He

This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool.

F. Richard Yu

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