Books in the Foundations and Trends (R) in Communications and Information Theory series

Explores the applications of information theory concepts in statistics, in the finite alphabet setting. The topics covered include large deviations, hypothesis testing, maximum likelihood estimation in exponential families, analysis of contingency tables, and iterative algorithms with an "information geometry" background.

Focuses on some of the key modern mathematical tools that are used for the derivation of concentration inequalities, on their links to information theory, and on their various applications to communications and coding. In addition to being a survey, this book also includes various new recent results derived by the authors.

Provides a tutorial review of the Lattice-Reduction-Aided and Integer-Forcing approaches to equalization in MIMO communications. The authors highlight the similarities and differences of both approaches while summarizing the various criteria for selecting the integer linear combinations available in the literature in a unified way.

The focus of this book is on the non-adaptive setting of group testing. In this setting, the test pools are designed in advance enabling them to be implemented in parallel. The book gives a comprehensive and thorough treatment of the subject from an information theoretic perspective, and covers several related developments.

Focuses on the fundamental underlying mathematical models, into a powerful framework for performing optimization of caching systems. In doing so, the authors present a background for the anticipated explosion in caching research, and provide a didactic view into how engineers have managed to infuse mathematical models into the study of caching.

Introduces the novel concept of Coded Computing. Coded Computing exploits coding theory to optimally inject and leverage data/task redundancy in distributed computing systems, creating coding opportunities to overcome the bottlenecks.

Surveys both classical literature and recent developments on the mismatched decoding problem, with an emphasis on achievable random-coding rates for memoryless channels. In doing so they present two widely-considered achievable rates known as the generalized mutual information and the LM rate, and overview their derivations and properties.

Addresses several variants of a general adversarial binary detection problem, depending on the knowledge available to the Defender and the Attacker of the statistical characterization of a system. The authors lead the reader through the considerations and solutions under two hypotheses, using a framework that can be adopted in many applications.

Addresses several network communication problems which can be considered as building blocks of networks. The book considers these problems from both the data transmission and the data storage perspectives, and devises structured coding schemes for the finite alphabet cases of these problems.