A unified graphical approach to random coding for multi-terminal networks

TL;DR

This paper introduces a unified graphical framework for deriving rate regions in multi-terminal networks, combining user virtualization, coding strategies, and graphical models to analyze and optimize communication performance.

Contribution

It presents a novel unified approach using graphical models to analyze and derive rate regions for multi-terminal networks with various coding strategies.

Findings

Unified framework for rate region derivation

Graphical Markov model for coding dependencies

Numerical optimization over rate-splitting strategies

Abstract

A unified approach to the derivation of rate regions for single-hop memoryless networks is presented. A general transmission scheme for any memoryless, single-hop, k-user channel with or without common information, is defined through two steps. The first step is user virtualization: each user is divided into multiple virtual sub-users according to a chosen rate-splitting strategy which preserves the rates of the original messages. This results in an enhanced channel with a possibly larger number of users for which more coding possibilities are available. Moreover, user virtualization provides a simple mechanism to encode common messages to any subset of users. Following user virtualization, the message of each user in the enhanced model is coded using a chosen combination of coded time-sharing, superposition coding and joint binning. A graph is used to represent the chosen coding strategies: nodes in the graph represent codewords while edges represent coding operations. This graph is used to construct a graphical Markov model which illustrates the statistical dependency among codewords that can be introduced by the superposition coding or joint binning. Using this statistical representation of the overall codebook distribution, the error probability of the code is shown to vanish via a unified analysis. The rate bounds that define the achievable rate region are obtained by linking the error analysis to the properties of the graphical Markov model. This proposed framework makes it possible to numerically obtain an achievable rate region by specifying a user virtualization strategy and describing a set of coding operations. The largest achievable rate region can be obtained by considering all the possible rate-splitting strategies and taking the union over all the possible ways to superimpose or bin codewords.