This book brings together papers from the 2018 International Conference on Communications, Signal Processing, and Systems, which was held in Dalian, China on July 14–16, 2018. Presenting the latest developments and discussing the interactions and links between these multidisciplinary fields, the book spans topics ranging from communications, signal processing and systems. It is aimed at undergraduate and graduate electrical engineering, computer science and mathematics students, researchers and engineers from academia and industry as well as government employees.

A self-contained guide to coordinated multi-point (CoMP), this comprehensive book covers everything from theoretical basics to practical implementation. Addressing a wide range of topics, it highlights the potential gains of CoMP, the fundamental degrees of freedom involved and the key challenges of using CoMP in practice. The editors and contributors bring unique real-world experience from running the world's first and largest test beds for LTE-Advanced, and recent field trial results from these tests are presented. With detailed insight into the realistic potential of CoMP as a key technology for LTE-Advanced and beyond, this is a must-read resource for professionals and students who want the big picture on CoMP or require in-depth knowledge of how to build cellular communication systems for the future.

A timely addition to the understanding of IMT-Advanced, this book places particular emphasis on the new areas which IMT-Advanced technologies rely on compared with their predecessors. These latest areas include Radio Resource Management, Carrier Aggregation, improved MIMO support and Relaying. Each technique is thoroughly described and illustrated before being surveyed in context of the LTE-Advanced standards. The book also presents state-of-the-art information on the different aspects of the work of standardization bodies (such as 3GPP and IEEE), making global links between them. Explores the latest research innovations to assess the future of the LTE standard Covers the latest research techniques for beyond IMT-Advanced such as Coordinated multi-point systems (CoMP), Network Coding, Device-to-Device and Spectrum Sharing Contains key information for researchers from academia and industry, engineers, regulators and decision makers working on LTE-Advanced and beyond

Cooperative devices and mechanisms are increasingly important to enhance the performance of wireless communications and networks, with their ability to decrease power consumption and packet loss rate and increase system capacity, computation, and network resilience. Considering the wide range of applications, strategies, and benefits associated wit

A unified treatment of the latest game theoretic approaches for designing, modeling, and optimizing emerging wireless communication networks. Covering theory, analytical tools, and applications, it is ideal for researchers and graduate students in academia and industry designing efficient, scalable and robust protocols for future wireless networks.

Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.

This book is one of the most comprehensive and up-to-date books written on Energy Efficiency. The readers will learn about different technologies for energy efficiency policies and programs to reduce the amount of energy. The book provides some studies and specific sets of policies and programs that are implemented in order to maximize the potential for energy efficiency improvement. It contains unique insights from scientists with academic and industrial expertise in the field of energy efficiency collected in this multi-disciplinary forum.