Matched Multiuser Gaussian Source-Channel Communications via Uncoded Schemes

TL;DR

This paper explores the optimality of uncoded schemes for transmitting correlated Gaussian sources over multiuser Gaussian channels, identifying conditions where these schemes achieve the boundary of the achievable distortion region.

Contribution

It introduces a novel decision problem approach to determine when uncoded schemes are optimal, simplifying the analysis of complex multiuser Gaussian source-channel problems.

Findings

Optimality of uncoded schemes under certain channel conditions

New outer bounds for multiuser Gaussian source-channel problems

Decoupling complex problems into simpler sub-problems

Abstract

We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean squared error distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the source, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean squared error distortion measure. It is shown that when the channel parameters match certain general conditions, the induced distortion tuples are on the boundary of the achievable distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouple the overall problem into several simpler sub-problems. Optimality results previously unknown in the literature are obtained using this novel approach. As a byproduct of the investigation, novel outer bounds are derived for these two problems.