The Gaussian many-help-one distributed source coding problem

Saurabha Tavildar, Pramod Viswanath, Aaron B. Wagner

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Jointly Gaussian memoryless sources are observed at Ν distinct terminals. The goal is to efficiently encode the observations in a distributed fashion so as to enable reconstruction of any one of the observations, say the first one, at the decoder subject to a quadratic fidelity criterion. Our main result is a precise characterization of the rate-distortion region when the covariance matrix of the sources satisfies a "tree-structure" condition. In this situation, a natural analog-digital separation scheme optimally trades off the distributed quantization rate tuples and the distortion in the reconstruction: Each encoder consists of a point-to-point Gaussian vector quantizer followed by a Slepian-Wolf binning encoder. We also provide a partial converse that suggests that the tree-structure condition is fundamental.

Original languageEnglish (US)
Article number5361477
Pages (from-to)564-581
Number of pages18
JournalIEEE Transactions on Information Theory
Issue number1
StatePublished - Jan 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

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


  • Entropy power inequality
  • Gaussian sources
  • Many-help-one problem
  • Network source coding
  • Rate distortion
  • Tree sources


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