Cross-Layer Resource Allocation in Wireless Communications offers practical techniques and models for the design and optimisation of cross-layer resource allocation – one of the hottest topics in wireless communications. Resource allocation in wireless networks is traditionally approached either through information theory or communications networks. To break down the barriers between these distinct approaches, this book bridges the physical and network layers by providing cross-layer resource allocation techniques, models, and methodologies. Its unique approach allows optimisation of network resources and will enable engineers to improve signal quality, enhance network and spectrum utilization, increase throughput, and solve the problem of shadowing. Topics covered include different views of spectral efficiency, the role of spatial diversity, of delay in resource allocation, and possible extensions to OFDMA systems. This will be an ideal reference on cross-layer resource allocation between the PHY and MAC layers for R&D and network design engineers and researchers in universities dealing with sensor networks and cognitive systems. - Gives a full description of the characteristics of the PHY layer that promote efficient resource allocation strategies - Gives special emphasis on cross-layer design for spatial diversity schemes - Provides a framework for interaction between the PHY and MAC layers, their parameters of performance and their relationship - Presents resource allocation as a cross-layer design based on an optimization of MAC layer parameters with an accurate model of the PHY layer

Information flow in a telecommunication network is accomplished through the interaction of mechanisms at various design layers with the end goal of supporting the information exchange needs of the applications. In wireless networks in particular, the different layers interact in a nontrivial manner in order to support information transfer. In this text we will present abstract models that capture the cross-layer interaction from the physical to transport layer in wireless network architectures including cellular, ad-hoc and sensor networks as well as hybrid wireless-wireline. The model allows for arbitrary network topologies as well as traffic forwarding modes, including datagrams and virtual circuits. Furthermore the time varying nature of a wireless network, due either to fading channels or to changing connectivity due to mobility, is adequately captured in our model to allow for state dependent network control policies. Quantitative performance measures that capture the quality of service requirements in these systems depending on the supported applications are discussed, including throughput maximization, energy consumption minimization, rate utility function maximization as well as general performance functionals. Cross-layer control algorithms with optimal or suboptimal performance with respect to the above measures are presented and analyzed. A detailed exposition of the related analysis and design techniques is provided.

Although the existing layering infrastructure--used globally for designing computers, data networks, and intelligent distributed systems and which connects various local and global communication services--is conceptually correct and pedagogically elegant, it is now well over 30 years old has started create a serious bottleneck. Using Cross-Layer Techniques for Communication Systems: Techniques and Applications explores how cross-layer methods provide ways to escape from the current communications model and overcome the challenges imposed by restrictive boundaries between layers. Written exclusively by well-established researchers, experts, and professional engineers, the book will present basic concepts, address different approaches for solving the cross-layer problem, investigate recent developments in cross-layer problems and solutions, and present the latest applications of the cross-layer in a variety of systems 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.

Following the pattern of the Internet growth in popularity, started in the early 1990s, the current unprecedented expansion of wireless technology promises to have an even greater effect on how people communicate and interact, with considerable socio-economic impact all over the world. The driving force behind this growth is the remarkable progress in component miniaturization, integration, and also devel- ments in waveforms, coding, and communication protocols. Besides established infrastructurebased wireless networks (cellular, WLAN, sat- lite) ad-hoc wireless networks emerge as a new platform for distributed applications and for personal communication in scenarios where deploying infrastructure is not feasible. In ad-hoc wireless networks, each node is capable of forwarding packets on behalf of other nodes, so that multi-hop paths provide end-to-end connectivity. The increased flexibility and mobility of ad-hoc wireless networks are favored for appli- tions in law enforcement, homeland defense and military. In a world where wireless networks become increasingly interoperable with each other and with the high-speed wired Internet, personal communication systems will transform into universal terminals with instant access to variate content and able of handle demanding tasks, such as multimedia and real-time video. With users roaming between networks, and with wide variation in wireless link quality even in a single domain, the communications terminal must continue to provide a level of Quality of Service that is acceptable to the user and conforms to a contracted Service Level Agreement.

This book allows readers to gain an in-depth understanding of resource allocation problems in wireless networks and the techniques used to solve them.

Adaptive techniques play a key role in modern wireless communication systems. The concept of adaptation is emphasized in the Adaptation in Wireless Communications Series through a unified framework across all layers of the wireless protocol stack ranging from the physical layer to the application layer, and from cellular systems to next-generation wireless networks. Adaptation and Cross Layer Design in Wireless Networks is devoted to adaptation in the data link layer, network layer, and application layer. The book presents state-of-the-art adaptation techniques and methodologies, including cross-layer adaptation, joint signal processing, coding and networking, selfishness in mobile ad hoc networks, cooperative and opportunistic protocols, adaptation techniques for multimedia support, self –organizing routing, and tunable security services. It presents several new theoretical paradigms and analytical findings which are supported with various simulation and experimental results. Adaptation in wireless communications is needed in order to achieve high capacity and ubiquitous communications. The current trend in wireless communication systems is to make adaptation dependent upon the state of the relevant parameters in all layers of the system. Focusing on simplified cross layer design approaches, this volume describes advanced techniques such as adaptive resource management, adaptive modulation and coding, 4G communications, QoS, diversity combining, and energy and mobility aware MAC protocols. The first volume in the series, Adaptive Signal Processing in Wireless Communications (cat no.46012) covers adaptive signal processing at the physical layer.

Adaptive techniques play a key role in modern wireless communication systems. The concept of adaptation is emphasized in the Adaptation in Wireless Communications Series through a unified framework across all layers of the wireless protocol stack ranging from the physical layer to the application layer, and from cellular systems to next-generation wireless networks. Adaptation and Cross Layer Design in Wireless Networks is devoted to adaptation in the data link layer, network layer, and application layer. The book presents state-of-the-art adaptation techniques and methodologies, including cross-layer adaptation, joint signal processing, coding and networking, selfishness in mobile ad hoc networks, cooperative and opportunistic protocols, adaptation techniques for multimedia support, self –organizing routing, and tunable security services. It presents several new theoretical paradigms and analytical findings which are supported with various simulation and experimental results. Adaptation in wireless communications is needed in order to achieve high capacity and ubiquitous communications. The current trend in wireless communication systems is to make adaptation dependent upon the state of the relevant parameters in all layers of the system. Focusing on simplified cross layer design approaches, this volume describes advanced techniques such as adaptive resource management, adaptive modulation and coding, 4G communications, QoS, diversity combining, and energy and mobility aware MAC protocols. The first volume in the series, Adaptive Signal Processing in Wireless Communications (cat no.46012) covers adaptive signal processing at the physical layer.

The rapid growth in mobile communications has led to an increasing demand for wideband high data rate communications services. In recent years, the Distributed Antenna System (DAS) has emerged as a promising candidate beyond 3G and 4G mobile communications. Distributed Antenna Systems: Open Architecture for Future Wireless Communications is

Offers practitioners, researchers, and academicians with fundamental principles of cooperative communication. This book provides readers diverse findings and exposes underlying issues in the analysis, design, and optimization of wireless systems.