Modern, current, and future communications/processing aspects motivate basic information-theoretic research for a wide variety of systems for which we do not have the ultimate theoretical solutions (for example, a variety of problems in network information theory as the broadcast/interference and relay channels, which mostly remain unsolved in terms of determining capacity regions and the like). Technologies such as 5/6G cellular communications, Internet of Things (IoT), and mobile edge networks, among others, not only require reliable rates of information measured by the relevant capacity and capacity regions, but are also subject to issues such as latency vs. reliability, availability of system state information, priority of information, secrecy demands, energy consumption per mobile equipment, sharing of communications resources (time/frequency/space), etc. This book, composed of a collection of papers that have appeared in the Special Issue of the Entropy journal dedicated to “Information Theory for Data Communications and Processing”, reflects, in its eleven chapters, novel contributions based on the firm basic grounds of information theory. The book chapters address timely theoretical and practical aspects that constitute both interesting and relevant theoretical contributions, as well as direct implications for modern current and future communications systems.

This book constitutes the joint refereed proceedings of the 20th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2020, and the 13th Conference on Internet of Things and Smart Spaces, ruSMART 2020. The conference was held virtually due to the COVID-19 pandemic. The 79 revised full papers presented were carefully reviewed and selected from 225 submissions. The papers of NEW2AN address various aspects of next-generation data networks, with special attention to advanced wireless networking and applications. In particular, they deal with novel and innovative approaches to performance and efficiency analysis of 5G and beyond systems, employed game-theoretical formulations, advanced queuing theory, and stochastic geometry, while also covering the Internet of Things, cyber security, optics, signal processing, as well as business aspects. ruSMART 2020, provides a forum for academic and industrial researchers to discuss new ideas and trends in the emerging areas.

Meet the wireless security challenges of the future with this key volume The 6th generation of wireless communication technology—known as 6G—promises to bring both revolutionary advances and unique challenges. Secure communications will be harder than ever to achieve under the new integrated ground, air, and space networking paradigm, with increased connectivity creating the potential for increased vulnerability. Physical-layer security, which draws upon the physical properties of the channel or network to secure information, has emerged as a promising solution to these challenges. Physical-Layer Security for 6G provides a working introduction to these technologies and their burgeoning wireless applications. With particular attention to heterogeneous and distributed network scenarios, this book offers both the information-theory fundamentals and the most recent developments in physical-layer security. It constitutes an essential resource for meeting the unique security challenges of 6G. Physical-Layer Security for 6G readers will also find: Analysis of physical-layer security in the quality of security framework (QoSec) Detailed discussion of physical-layer security applications in visible light communication (VLC), intelligence reflecting surface (IRS), and more Practical use cases and demonstrations Physical-Layer Security for 6G is ideal for wireless research engineers as well as advanced graduate students in wireless technology.

This book is an extensively elaborated treatise on sophisticated channel coding for advanced mobile communications. Setting out from the author's own experience of university teaching for over three decades, the book covers the most fundamental aspects of sophisticated channel coding ranging for advanced mobile communications beginning with general concepts of information theory and number theory, block coding including cyclic and convolutional codes, important aspects of permutation matrices and Kronecker products and Reed-Muller codes, over convolutional turbo codes and low density parity check (LDPC) codes to polar codes.

This volume includes the main contributions by the plenary speakers from the ISAAC congress held in Aveiro, Portugal, in 2019. It is the purpose of ISAAC to promote analysis, its applications, and its interaction with computation. Analysis is understood here in the broad sense of the word, including differential equations, integral equations, functional analysis, and function theory. With this objective, ISAAC organizes international Congresses for the presentation and discussion of research on analysis. The plenary lectures in the present volume, authored by eminent specialists, are devoted to some exciting recent developments in topics such as science data, interpolating and sampling theory, inverse problems, and harmonic analysis.

Understand fundamental principles of ambient backscatter technology and their diverse potential applications with this authoritative review.

The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence.