Lossless Coding of Correlated Sources With Actions

Oron Sabag, Haim H. Permuter, Asaf Cohen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

This paper studies the problem of the distributed compression of correlated sources with an action-dependent joint distribution. This class of problems is, in fact, an extension of the Slepian-Wolf model, but where cost-constrained actions taken by the encoder or the decoder affect the generation of one of the sources. The purpose of this paper is to study the impact of actions on the achievable rates. In particular, two cases where transmission occurs over a rate-limited link are studied; case A for actions taken at the decoder and case B where actions are taken at the encoder. A complete single-letter characterization of the set of achievable rates is given in both cases. Furthermore, a network coding setup for the case where actions are taken at the encoder is investigated. The sources are generated at different nodes of the network and are required at a set of terminal nodes, yet transmission occurs over a general, acyclic, directed network. For this setup, generalized cut-set bounds are derived, and a full characterization of the set of achievable rates using single-letter expressions is provided. For this scenario, random linear network coding is proved to be optimal, even though this is not a classical multicast problem. In addition, two binary examples are investigated and demonstrate how actions taken at different nodes of the system have a significant effect on the achievable rate region, when compared with a naive time-sharing strategy.

Original languageAmerican English
Article number7570176
Pages (from-to)1237-1252
Number of pages16
JournalIEEE Transactions on Information Theory
Volume63
Issue number2
DOIs
StatePublished - Feb 2017
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • Actions
  • Slepian-Wolf source coding
  • correlated sources
  • distributed compression
  • network coding
  • random linear network coding

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