OMP based joint sparsity pattern recovery under communication constraints

Thakshila Wimalajeewa, Pramod K. Varshney

Research output: Contribution to journalArticle

37 Scopus citations

Abstract

We address the problem of joint sparsity pattern recovery based on multiple measurement vectors (MMVs) in resource constrained distributed networks. We assume that distributed nodes observe sparse signals that share a common (but unknown) sparsity pattern. Each node is assumed to sample the sparse signals via different sensing matrices in general. In many distributed communication networks, it is often required that joint sparse recovery be performed under inherent resource constraints such as communication bandwidth and transmit/processing power. We propose two approaches to take the communication constraints into account while performing joint sparsity pattern recovery. First, we explore the use of a shared multiple access channel (MAC) in forwarding observation vectors from each node to a fusion center. With MAC, while the bandwidth requirement does not depend on the number of nodes, the fusion center has access to only linear combinations of the observations. We discuss the conditions under which the common sparsity pattern can be recovered reliably. Second, we develop two efficient collaborative algorithms based on orthogonal matching pursuit (OMP), to jointly estimate the common sparsity pattern in a decentralized manner with a low communication overhead. In the proposed algorithms, each node collaborates with neighboring nodes by sharing a small amount of information at different stages while estimating the indices of the true sparsity pattern in a greedy manner. The tradeoff between the performance gain and the communication overhead of the proposed algorithms is demonstrated via simulations.

Original languageEnglish (US)
Article number6867383
Pages (from-to)5059-5072
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume62
Issue number19
DOIs
StatePublished - Oct 1 2014

Keywords

  • Multiple measurement vectors (MMVs)
  • compressive sensing
  • decentralized algorithms
  • orthogonal matching pursuit (OMP)
  • sparsity pattern recovery

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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