CIF: Small: Collaborative Research:A Reductionist View of Network Information Theory

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Advances in modern society are increasingly intertwined with those of our communication systems. Everything from entertainment to business to transportation to healthcare itself relies on our ability to communicate efficiently and reliably. With progress in all of these domains come increasing communication demands. The key to meeting those demands is an ever increasing and growing understanding of how to build better communication networks and how to operate the ones that we have more effectively. While much is known about small parts of our communication networks, surprisingly little is known about how to operate the network as a whole more efficiently. This project sets out to tackle that monumental and critical goal, by seeking to uncover the implications of network design choices, understand the commonalities and differences among these implications, and expand the theory and techniques needed both to operate these systems efficiently and to understand the limits of their performance.The network information theory literature considers code design and performance limits for communication systems under a wide variety of system models and constraints. While a careful reading of the literature reveals many common tools and strategies, each new problem engenders its own new theory. This work takes a systematic approach towards uncovering the hidden underlying commonalities that connect the solutions of information theoretic problems. Central to this approach are reduction arguments that derive relationships between the solutions to different information theoretic problems. In such, our framework shifts attention from the traditional focus on solving example networks to a focus on building connections between problem solutions. The work is organized in three thrusts. The first examines network modeling assumptions with the goal of determining how much each assumption impacts the information theoretic solvability of networks, distilling out aspects that have little or no impact on solvability, and simplifying broad classes of communication problems down to their essential representative core. The second thrust moves from individual network characteristics to comparisons between characteristics in order to understand the common, unsolved challenges that lie at the heart of a wide range of network information theoretic questions and use these commonalities to develop a taxonomy of problems and solutions. Finally, as the first two thrusts steer attention towards representative examples and common challenges, existing tools for code design and capacity calculation are enhanced and amplified by applying them to new communication scenarios for which reductive arguments demonstrate a connection.