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.

Information .ow 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 di erent layers interact in a nontrivial manner in order to support information transfer. In this paper 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 tra c forwarding modes, including datagrams and virtual cir- cuits. 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.

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.

In this dissertation, we investigate problems of resource allocation for uplink multiuser wireless communication channels. The users want to upload compressed video streams with different rate-distortion (RD) characteristics to a base station via a multi-carrier channel. The video is organized in groups of pictures (GOP), and the base station makes resource allocation decisions based jointly on the channel state information (CSI) and the RD information to minimize the average video distortion across all users. We first study the case where the channel is slowly varying. With the assumption of the channel staying constant over the duration of a GOP, we derive an optimal condition for minimizing the sum of distortions in a setting of continuous frequency channel response. Our result shows that for a specific band to be assigned to a particular user, the product of the slope of the RD curve of that user and a marginal rate increment corresponding to that band should be maximized. We then design an algorithm which iteratively calculates the status of the RD information and the CSI in a block fading setting. Compared to two baseline resource allocation algorithms that use only a single layer of information, the simulation results show that the cross layer scheme can consistently improve the performance of the system. We then focus on the case of a channel with arbitrary mobility. To better utilize the time varying channel, we design a pilot symbol assisted modulation (PSAM) system which periodically adapts the modulation format according to the instantaneous CSI. We show that the two baseline algorithms with single layer information behave quite differently as the Doppler spread changes, but the cross layer algorithm performs robustly across all different mobile speeds. We further characterize the capacity gain of using the cross layer scheme.

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.

Merging the fundamental principles of resource allocation with the state-of-the-art in research and application examples, Han and Liu present a novel and comprehensive perspective for improving wireless systems performance. Cross-layer multiuser optimization in wireless networks is described systematically. Starting from the basic principles, such as power control and multiple access, coverage moves to the optimization techniques for resource allocation, including formulation and analysis, and game theory. Advanced topics such as dynamic resource allocation and resource allocation in antenna array processing, and in cooperative, sensor, personal area, and ultrawideband networks, are then discussed. Unique in its scope, timeliness, and innovative author insights, this invaluable work will help graduate students and researchers to understand the basics of wireless resource allocation whilst highlighting modern research topics, and will help industrial engineers to improve system optimization.