Design Methods For Optimal Resource Allocation In Wireless Networks

The purpose of this book is to provide tools for a better understanding of the fundamental tradeo?s and interdependencies in wireless networks, with the goal of designing resource allocation strategies that exploit these int- dependencies to achieve signi?cant performance gains. Two facts prompted us to write it: First, future wireless applications will require a fundamental understanding of the design principles and control mechanisms in wireless networks. Second, the complexity of the network problems simply precludes the use of engineering common sense alone to identify good solutions, and so mathematics becomes the key avenue to cope with central technical problems in the design of wireless networks. In this book, two ?elds of mathematics play a central role: Perron-Frobenius theory for non-negative matrices and optimization theory. This book is a revised and expanded version of the research monograph “Resource Allocation in Wireless Networks” that was published as Lecture Notes in Computer Sciences (LNCS 4000) in 2006. Although the general structure has remained unchanged to a large extent, the book contains - merous additional results and more detailed discussion. For instance, there is a more extensive treatment of general nonnegative matrices and interf- ence functions that are described by an axiomatic model. Additional material on max-min fairness, proportional fairness, utility-based power control with QoS (quality of service) support and stochastic power control has been added.

The wireless industry is in the midst of a fundamental shift from providing voice-only services to offering customers an array of multimedia services, including a wide variety of audio, video and data communications capabilities. Future wireless networks will be integrated into every aspect of daily life, and therefore could affect our life in a magnitude similar to that of the Internet and cellular phones. This monograph demonstrates that these emerging applications and directions require fundamental understanding on how to design and control wireless networks that lies far beyond what the currently existing theory can provide. It is shown that mathematics is the key technology to cope with central technical problems in the design of wireless networks since the complexity of the problem simply precludes the use of engineering common sense alone to identify good solutions. The main objective of this book is to provide tools for better understanding the fundamental tradeoffs and interdependencies in wireless networks, with the goal of designing resource allocation strategies that exploit these interdependencies to achieve significant performance gains. The book consists of three largely independent parts: theory, applications and appendices. The latter contain foundational apects to make the book more understandable to readers who are not familiar with some basic concepts and results from linear algebra and convex analysis.

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

Wireless network localization (WNL) is an emerging paradigm for providing high-accuracy positional information in GPS-challenged environments. The localization performance of a node in WNL is determined by the allocation of transmit resources among its neighboring nodes. To achieve the best localization performance, we develop a computational geometry framework for optimal resource allocation in WNL. We first determine an affine map that transforms each resource allocation strategy into a point in 3-D Euclidian space. By exploiting geometric properties of these image points, we prove the sparsity property of the optimal resource allocation vector, i.e., the optimal localization performance can be achieved by allocating resources to only a small subset of neighboring nodes. Moreover, these geometric properties enable the reduction of the search space for optimal solutions, based on which we design efficient resource allocation strategies. Numerical results show that the proposed strategies can achieve significant improvements in both localization performance and computation efficiency. Our approach provides a new methodology for resource allocation in network localization, yielding exact optimal solutions rather than e-approximate solutions.

Do you need to improve wireless system performance? Learn how to maximise the efficient use of resources with this systematic and authoritative account of wireless resource management. Basic concepts, optimization tools and techniques, and application examples, are thoroughly described and analysed, providing a unified framework for cross-layer optimization of wireless networks. State-of-the-art research topics and emerging applications, including dynamic resource allocation, cooperative networks, ad hoc/personal area networks, UWB, and antenna array processing, are examined in depth. If you are involved in the design and development of wireless networks, as a researcher, graduate student or professional engineer, this is a must-have guide to getting the best possible performance from your network.

Optimal Resource Allocation in Coordinated Multi-Cell Systems provides a solid grounding and understanding for optimization of practical multi-cell systems and will be of interest to all researchers and engineers working on the practical design of such systems.

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.

Tackling problems from the least complicated to the most, Resource Allocation in Uplink OFDMA Wireless Systems provides readers with a comprehensive look at resource allocation and scheduling techniques (for both single and multi-cell deployments) in uplink OFDMA wireless networks relying on convex optimization and game theory to thoroughly analyze performance. Inside, readers will find topics and discussions on: Formulating and solving the uplink ergodic sum-rate maximization problem Proposing suboptimal algorithms that achieve a close performance to the optimal case at a considerably reduced complexity and lead to fairness when the appropriate utility is used Investigating the performance and extensions of the proposed suboptimal algorithms in a distributed base station scenario Studying distributed resource allocation where users take part in the scheduling process, and considering scenarios with and without user collaboration Formulating the sum-rate maximization problem in a multi-cell scenario, and proposing efficient centralized and distributed algorithms for intercell interference mitigation Discussing the applicability of the proposed techniques to state-of-the-art wireless technologies, LTE and WiMAX, and proposing relevant extensions Along with schematics and figures featuring simulation results, Resource Allocation in Uplink OFDMA Wireless Systems is a valuable book for?wireless communications and cellular systems professionals and students.