One-Shot Coding and Applications

TL;DR

This paper develops one-shot information theory by exploring explicit coding schemes for single-use sources and channels, extending Poisson functional representation techniques to complex scenarios beyond traditional applications.

Contribution

It extends Poisson functional representation methods to more complex one-shot coding scenarios, broadening their applicability in information theory.

Findings

Derived one-shot achievability results that recover asymptotic behaviors.

Extended Poisson functional representation to complex scenarios.

Unified coding schemes for a broad class of problems.

Abstract

One-shot information theory addresses scenarios in source coding and channel coding where the signal blocklength is assumed to be 1. In this case, each source and channel can be used only once, and the sources and channels are arbitrary and not required to be memoryless or ergodic. We study the achievability part of one-shot information theory, i.e., we consider explicit coding schemes in the oneshot scenario. The objective is to derive one-shot achievability results that can imply existing (first-order and second-order) asymptotic results when applied to memoryless sources and channels, or applied to systems with memory that behave ergodically. Poisson functional representation was first proposed as a one-shot channel simulation technique by Li and El Gamal [118] for proving a strong functional representation lemma. It was later extended to the Poisson matching lemma by Li and Anantharam [117], which provided a unified one-shot coding scheme for a broad class of information-theoretic problems. The main contribution of this thesis is to extend the applicability of Poisson functional representation to various more complicated scenarios, where the original version cannot be applied directly and further extensions must be developed.