This is the first comprehensive book on the AIMD algorithm, the most widely used method for allocating a limited resource among competing agents without centralized control. The authors offer a new approach that is based on positive switched linear systems. It is used to develop most of the main results found in the book, and fundamental results on stochastic switched nonnegative and consensus systems are derived to obtain these results. The original and best known application of the algorithm is in the context of congestion control and resource allocation on the Internet, and readers will find details of several variants of the algorithm in order of increasing complexity, including deterministic, random, linear, and nonlinear versions. In each case, stability and convergence results are derived based on unifying principles. Basic and fundamental properties of the algorithm are described, examples are used to illustrate the richness of the resulting dynamical systems, and applications are provided to show how the algorithm can be used in the context of smart cities, intelligent transportation systems, and the smart grid.

This book presents a mathematical treatment of the radio resource allocation of modern cellular communications systems in contested environments. It focuses on fulfilling the quality of service requirements of the living applications on the user devices, which leverage the cellular system, and with attention to elevating the users’ quality of experience. The authors also address the congestion of the spectrum by allowing sharing with the band incumbents while providing with a quality-of-service-minded resource allocation in the network. The content is of particular interest to telecommunications scheduler experts in industry, communications applications academia, and graduate students whose paramount research deals with resource allocation and quality of service.

Distributed Sensor Networks is the first book of its kind to examine solutions to this problem using ideas taken from the field of multiagent systems. The field of multiagent systems has itself seen an exponential growth in the past decade, and has developed a variety of techniques for distributed resource allocation. Distributed Sensor Networks contains contributions from leading, international researchers describing a variety of approaches to this problem based on examples of implemented systems taken from a common distributed sensor network application; each approach is motivated, demonstrated and tested by way of a common challenge problem. The book focuses on both practical systems and their theoretical analysis, and is divided into three parts: the first part describes the common sensor network challenge problem; the second part explains the different technical approaches to the common challenge problem; and the third part provides results on the formal analysis of a number of approaches taken to address the challenge problem.

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.

During the period of the grant, 6/15/95-6/30/98, we investigated problems in major areas: (1) Image Analysis Tasks, including image interpretation, object recognition, and tracking; (2) Distributed communication networks and distributed resource allocation. In the former area, we developed and explored three paradigms: hierarchical compositional models; deformable templates for a host of applications including medical tasks; and HMM/deformable templates for tracking and recognition of moving objects. In the later area, we developed a novel scheme for managing buffer overflows; a new framework for network security; and a mathematical framework for synthesizing distributed algorithms.

Market-Based Control is a paradigm for controlling complex systems that would otherwise be very difficult to control, maintain, or expand. The purpose of this volume is to illustrate the utility of market-based control through a series of papers focusing on different applications. This volume, for the first time, brings together the research from a wide range of fields all using a market-based conceptual framework. The features of markets that have provided motivation for these works include decentralization, interacting agents, and some notion of a resource that needs to be allocated. The papers span a range including theoretical considerations, simulations, and implementations.

This book investigates the resource allocation in backscatter-assisted communication networks. With the development of backscatter communications, integrating backscatter communication technology into traditional communication networks can improve the network performance significantly. To fully improve the performance of backscatter-assisted communication networks, resource allocation is of special importance. It is worth to mention that the resource allocation in backscatter-assisted communication networks is more challenging than that in traditional communication networks, and the tradeoff of the performance between backscatter communications and traditional communications needs to be carefully considered. In this book, considering that game theory is an attractive tool for developing and analyzing distributed, flexible, and autonomous networks, we develop the auction-based time scheduling schemes, contract-based time assignment scheme, and evolutionary game-based access point and service selection scheme for the backscatter-assisted radio-frequency-powered cognitive networks, where some important properties such as individual rationality are considered. We also employ the optimization approach and develop a relay mode selection and resource sharing scheme for backscatter-assisted hybrid relay networks to improve the throughput. We believe that the developed resource allocation schemes in this book can provide useful guidance for the design of backscatter-assisted communication networks and future Internet of Things. Graduate students, researchers, and engineers in the field of communication networks can benefit from the book.

While most books on the subject focus on resource allocation in just one type of network, this book is the first to examine the common characteristics of multiple distributed video communication systems. Comprehensive and systematic, Optimal Resource Allocation for Distributed Video Communication presents a unified optimization framework for resour

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.