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Provides a variety of practical optimization techniques and modeling tips for solving challenging wireless networking problems. Case studies show how the techniques can be applied in practice, homework exercises are given at the end of each chapter, and PowerPoint slides are available online, together with a solutions manual for instructors.
This book provides an introduction to opportunistic routing an emerging technology designed to improve the packet forwarding reliability, network capacity and energy efficiency of multihop wireless networks This book presents a comprehensive background to the technological challenges lying behind opportunistic routing. The authors cover many fundamental research issues for this new concept, including the basic principles, performance limit and performance improvement of opportunistic routing compared to traditional routing, energy efficiency and distributed opportunistic routing protocol design, geographic opportunistic routing, opportunistic broadcasting, and security issues associated with opportunistic routing, etc. Furthermore, the authors discuss technologies such as multi-rate, multi-channel, multi-radio wireless communications, energy detection, channel measurement, etc. The book brings together all the new results on this topic in a systematic, coherent and unified presentation and provides a much needed comprehensive introduction to this topic. Key Features: Addresses opportunistic routing, an emerging technology designed to improve the packet forwarding reliability, network capacity and energy efficiency of multihop wireless networks Discusses the technological challenges lying behind this new technology, and covers a wide range of practical implementation issues Explores many fundamental research issues for this new concept, including the basic principles of opportunistic routing, performance limits and performance improvement, and compares them to traditional routing (e.g. energy efficiency and distributed opportunistic routing protocol design, broadcasting, and security issues) Covers technologies such as multi-rate, multi-channel, multi-radio wireless communications, energy detection, channel measurement, etc. This book provides an invaluable reference for researchers working in the field of wireless networks and wireless communications, and Wireless professionals. Graduate students will also find this book of interest.
Do you need to know what technique to use to evaluate the reliability of an engineered system? This self-contained guide provides comprehensive coverage of all the analytical and modeling techniques currently in use, from classical non-state and state space approaches, to newer and more advanced methods such as binary decision diagrams, dynamic fault trees, Bayesian belief networks, stochastic Petri nets, non-homogeneous Markov chains, semi-Markov processes, and phase type expansions. Readers will quickly understand the relative pros and cons of each technique, as well as how to combine different models together to address complex, real-world modeling scenarios. Numerous examples, case studies and problems provided throughout help readers put knowledge into practice, and a solutions manual and Powerpoint slides for instructors accompany the book online. This is the ideal self-study guide for students, researchers and practitioners in engineering and computer science.
This book provides a comprehensive introduction to the underlying theory, design techniques and analytical results of wireless communication networks, focusing on the core principles of wireless network design. It elaborates the network utility maximization (NUM) theory with applications in resource allocation of wireless networks, with a central aim of design and the QoS guarantee. It presents and discusses state-of-the-art developments in resource allocation and performance optimization in wireless communication networks. It provides an overview of the general background including the basic wireless communication networks and the relevant protocols, architectures, methods and algorithms.
This book constitutes the refereed proceedings of the 12th International Conference on High-Performance Computing, HiPC 2005, held in Goa, India in December 2005. The 50 revised full papers presented were carefully reviewed and selected from 362 submissions. After the keynote section and the presentation of the 2 awarded best contributions the papers are organized in topical sections on algorithms, applications, architecture, systems software, communication networks, and 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.
A promising new technology, wireless mesh networks are playing an increasingly important role in the future generations of wireless mobile networks. Characterized by dynamic self-organization, self-configuration, and self-healing to enable quick deployment, easy maintenance, low cost, high scalability, and reliable services, this technology is beco
This SpringerBrief presents research in the application of Stochastic Petri Nets (SPN) to the performance evaluation of wireless networks under bursty traffic. It covers typical Quality-of-Service performance metrics such as mean throughput, average delay and packet dropping probability. Along with an introduction of SPN basics, the authors introduce the key motivation and challenges of using SPN to analyze the resource sharing performance in wireless networks. The authors explain two powerful modeling techniques that treat the well-known state space explosion problem: model decomposition and iteration, and model aggregation using stochastic high-level petri nets. The first technique assists in performance analysis of opportunistic scheduling, Device-to-Device communications with full frequency reuse and partial frequency reuse. The second technique is used to formulate a wireless channel mode for cross-layer performance analysis in OFDM system. Stochastic Petri Nets for Wireless Networks reveals useful insights for the design of radio resource management algorithms and a new line of thinking for the performance evaluation of future wireless networks. This material is valuable as a reference for researchers and professionals working in wireless networks and for advanced-level students studying wireless technologies in electrical engineering or computer science.
This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006. The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions.
In today's highly competitive and rapidly evolving global landscape, the quest for efficiency has become a crucial factor in determining the success of organizations across various industries. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency is a comprehensive guide that delves into the powerful mathematical tool of DEA, is designed to assess the relative efficiency of decision-making units (DMUs), and provides valuable insights for performance improvement. This book presents a systematic overview of DEA models and techniques, from fundamental concepts to advanced methods, showcasing their practical applications through real-world examples and case studies. Catering to a broad audience, this book is designed for students, researchers, consultants, decision-makers, and enthusiasts in the field of efficiency analysis and performance measurement. Consultants and practitioners will gain practical insights for applying DEA in various contexts, and decision-makers will be equipped to make informed decisions for maximizing efficiency. Additionally, individuals with a general interest in data analysis and performance measurement will find this book accessible and informative. This book covers a wide range of topics, including mathematical foundations of DEA, DEA models and variations, DEA efficiency and productivity measures, DEA applications in various industries such as healthcare, finance, supply chain management, environmental management, education management, and public sector management.