Cross Layer Resource Allocation In Cognitive Radio Networks Models Algorithms And Applications

Cross-Layer Resource Allocation in Cognitive Radio Networks: Models, Algorithms, and Applications

Cross-Layer Resource Allocation in Cognitive Radio Networks: Models, Algorithms, and Applications

This book is about cognitive radio (CR), a revolution in radio technology and an enabling technology for dynamic spectrum access. Due to the unique characteristics of the wireless networks, it is essential to address the approach of multiple layers (e.g., physical, link, and network) to maximize the network performance. The formulation of this cross-layer problem is usually complicated and challenging, while wireless resource allocation is a vital way to handle the race condition of the limited wireless resources. However, given the intrinsic characteristics of cognitive radio networks (CRN), none of the existing analytical approach could be a direct fit. Therefore, innovative theoretical results, along with the corresponding mathematical techniques, are necessary. In this book, we aim to develop some novel algorithmic design and optimization techniques that provide optimal or near-optimal solutions. Although cross-layer design has been introduced to CRN for many years, there are rarely any books for researchers, engineers, and students, from the engineering perspective. From one hand, most of the existing books primarily focus on the mathematical and economic aspects, which are considerably different from the engineering. On the other hand, all of the books mainly aim to system optimization or control techniques, while the cross-layer algorithm design in the distributed environment is usually ignored. As the result, there is an urgent demand for a reference source, which can provide complete information on how to fully adopt cross-layer resource allocation to the CRN. In this regard, this book not only focuses on the description of the main aspects of cross-layer resource allocation over CRN, but also provides a review of the application solutions. In a nutshell, it provides a specific treatment of cross-layer design in CRN. The topics range from the basic concepts of cross-layer resource allocation, to the state-of-the-art analyses, modelings, and optimizations for CRN.

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.