An information-theoretic approach to distributed compressed sensing

Dror Baron, Marco F. Duarte, Shriram Sarvotham, Michael B. Wakin, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

118 Scopus citations


Compressed sensing is an emerging field based on the revelation that a small group of linear projections of a sparse signal contains enough information for reconstruc-tion. In this paper we introduce a new theory for distributed compressed sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra-and inter-signal correlation structures. The DCS theory rests on a concept that we term the joint sparsity of a signal ensemble. We study a model for jointly sparse signals, propose algorithms for joint recovery of multi-ple signals from incoherent projections, and characterize the number of measure-ments per sensor required for accurate reconstruction. We establish a parallel with the Slepian-Wolf theorem from information theory and establish upper and lower bounds on the measurement rates required for encoding jointly sparse signals. In some sense DCS is a framework for distributed compression of sources with mem-ory, which has remained a challenging problem for some time. DCS is immediately applicable to a range of problems in sensor networks and arrays.

Original languageEnglish (US)
Title of host publication43rd Annual Allerton Conference on Communication, Control and Computing 2005
PublisherUniversity of Illinois at Urbana-Champaign, Coordinated Science Laboratory and Department of Computer and Electrical Engineering
Number of pages12
ISBN (Print)9781604234916
StatePublished - 2005
Event43rd Annual Allerton Conference on Communication, Control and Computing 2005 - Monticello, United States
Duration: Sep 28 2005Sep 30 2005


Other43rd Annual Allerton Conference on Communication, Control and Computing 2005
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


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