Matched multiuser Gaussian source channel communications via uncoded schemes

Chao Tian, Jun Chen, Suhas N. Diggavi, Shlomo Shamai

Research output: Contribution to journalArticlepeer-review


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 sources, 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 satisfy 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. Explicit and novel outer bounds are derived for the two problems as the byproducts of our investigation.

Original languageEnglish
Article number7920290
Pages (from-to)4155-4171
Number of pages17
JournalIEEE Transactions on Information Theory
Issue number7
StatePublished - Jul 2017


  • Broadcast
  • Combined sourcechannel coding
  • Multiple access

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


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