A Graph-based Framework for Transmission of Correlated Sources over Broadcast Channels

TL;DR

This paper introduces a graph-based framework for transmitting correlated sources over broadcast channels, using nearly semi-regular bipartite graphs to model source-channel interface and characterizing their exponential growth rates.

Contribution

It proposes a novel graph-based approach for joint source-channel coding in broadcast channels and provides an information-theoretic analysis of graph size growth rates.

Findings

Characterization of exponential growth rate of bipartite graph size for reliable transmission.

Analysis of graph size growth rate for representing correlated sources.

Framework bridges source coding and channel coding via bipartite graphs.

Abstract

In this paper we consider the communication problem that involves transmission of correlated sources over broadcast channels. We consider a graph-based framework for this information transmission problem. The system involves a source coding module and a channel coding module. In the source coding module, the sources are efficiently mapped into a nearly semi-regular bipartite graph, and in the channel coding module, the edges of this graph are reliably transmitted over a broadcast channel. We consider nearly semi-regular bipartite graphs as discrete interface between source coding and channel coding in this multiterminal setting. We provide an information-theoretic characterization of (1) the rate of exponential growth (as a function of the number of channel uses) of the size of the bipartite graphs whose edges can be reliably transmitted over a broadcast channel and (2) the rate of exponential growth (as a function of the number of source samples) of the size of the bipartite graphs which can reliably represent a pair of correlated sources to be transmitted over a broadcast channel.