Role Of Interference And Computational Complexity In Modern Wireless Networks

This book presents state-of-the-art research on robust resource allocation in current and future wireless networks. The authors describe the nominal resource allocation problems in wireless networks and explain why introducing robustness in such networks is desirable. Then, depending on the objectives of the problem, namely maximizing the social utility or the per-user utility, cooperative or competitive approaches are explained and their corresponding robust problems are considered in detail. For each approach, the costs and benefits of robust schemes are discussed and the algorithms for reducing their costs and improving their benefits are presented. Considering the fact that such problems are inherently non-convex and intractable, a taxonomy of different relaxation techniques is presented, and applications of such techniques are shown via several examples throughout the book. Finally, the authors argue that resource allocation continues to be an important issue in future wireless networks, and propose specific problems for future research.

Algorithmic discrete mathematics plays a key role in the development of information and communication technologies, and methods that arise in computer science, mathematics and operations research – in particular in algorithms, computational complexity, distributed computing and optimization – are vital to modern services such as mobile telephony, online banking and VoIP. This book examines communication networking from a mathematical viewpoint. The contributing authors took part in the European COST action 293 – a four-year program of multidisciplinary research on this subject. In this book they offer introductory overviews and state-of-the-art assessments of current and future research in the fields of broadband, optical, wireless and ad hoc networks. Particular topics of interest are design, optimization, robustness and energy consumption. The book will be of interest to graduate students, researchers and practitioners in the areas of networking, theoretical computer science, operations research, distributed computing and mathematics.

Wireless channels are becoming more and more important, with the future development of wireless ad-hoc networks and the integration of mobile and satellite communications. To this end, algorithmic detection aspects (involved in the physical layer) will become fundamental in the design of a communication system. This book proposes a unified approach to detection for stochastic channels, with particular attention to wireless channels. The core idea is to show that the three main criteria of sequence detection, symbol detection and graph-based detection, can all be described within a general framework. This implies that a detection algorithm based on one criterion can be extended to the other criteria in a systematic manner. Presents a detailed analysis of statistical signal detection for digital signals transmitted over wireless communications Provides a unifying framework for different signal detection algorithms, such as sequence detection, symbol detection and graph-based detection, important for the design of modern digital receivers operating over mobile channels Features the hot topic of graph-based detection Detection Algorithms for Wireless Communications represents a novel contribution with respect to the current literature, with a unique focus on detection algorithms, as such it will prove invaluable to researchers working in academia and industry and in the field of wireless communications, as well as postgraduate students attending advanced courses on mobile communications.

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.

Since interference is the main performance-limiting factor in most wireless networks, it is crucial to characterize the interference statistics. The main two determinants of the interference are the network geometry (spatial distribution of concurrently transmitting nodes) and the path loss law (signal attenuation with distance). For certain classes of node distributions, most notably Poisson point processes, and attenuation laws, closed-form results are available, for both the interference itself as well as the signal-to-interference ratios, which determine the network performance. This monograph presents an overview of these results and gives an introduction to the analytical techniques used in their derivation. The node distribution models range from lattices to homogeneous and clustered Poisson models to general motion-invariant ones. The analysis of the more general models requires the use of Palm theory, in particular conditional probability generating functionals, which are briefly introduced in the appendix.

The last decade has seen an unprecedented growth in the demand for wireless services. These services are fueled by applications that often require not only high data rates, but also very low latency to function as desired. However, as wireless networks grow and support increasingly large numbers of users, these control algorithms must also incur only low complexity in order to be implemented in practice. Therefore, there is a pressing need to develop wireless control algorithms that can achieve both high throughput and low delay, but with low-complexity operations. While these three performance metrics, i.e., throughput, delay, and complexity, are widely acknowledged as being among the most important for modern wireless networks, existing approaches often have had to sacrifice a subset of them in order to optimize the others, leading to wireless resource allocation algorithms that either suffer poor performance or are difficult to implement. In contrast, the recent results presented in this book demonstrate that, by cleverly taking advantage of multiple physical or virtual channels, one can develop new low-complexity algorithms that attain both provably high throughput and provably low delay. The book covers both the intra-cell and network-wide settings. In each case, after the pitfalls of existing approaches are examined, new systematic methodologies are provided to develop algorithms that perform provably well in all three dimensions.

This comprehensive technical guide explains game theory basics, architectures, protocols, security, models, open research issues, and cutting-edge advances and applications. Describing how to employ game theory in infrastructure-based wireless networks and multihop networks to reduce power consumption, it facilitates quick and easy reference to related optimization and algorithm methodologies. The book explains how to apply the game theoretic model to address resource allocation, congestion control, attacks, routing, energy management, packet forwarding, and MAC.

The integration of Artificial Intelligence (AI) with Communication Technologies (ICT) is a critical aspect of research and development today, but it poses numerous challenges and bottlenecks. To address these issues, the book AI and Its Convergence with Communication Technologies, edited by a team of expert scholars, provides a comprehensive overview of the state-of-the-art research and practical challenges related to AI's convergence with ICT. It is designed to benefit engineers, professionals, scientists, and academicians, providing them with insights into the ICT industry and research from an AI perspective. The book covers a wide range of topics, including recent advancements and applications in AI, AI in signal processing, AI in mobile and modern wireless networks, and AI towards automation. It also addresses potential applications of AI in national defense, military technology, hybrid warfare, medical and health sciences, and energy-efficient systems. Furthermore, the book highlights the strengths and weaknesses of AI convergence with ICT, along with emerging frontiers and recommendations. It provides a brief history of AI in ICT and a comprehensive introduction to ICT-related methods and techniques in artificial intelligence and machine learning. The book emphasizes the role of AI in extracting knowledge and making predictions in decision-making strategies for businesses, management, and governance. Overall, this book offers a significant contribution to the understanding of AI and its convergence with communication technologies, making it a must-read for scholars and researchers who seek to understand the intersection of AI and ICT and how it impacts modern industries and research.