Distributed compressed sensing for static and time-varying networks

Stacy Patterson, Yonina C. Eldar, Idit Keidar

Research output: Contribution to journalArticlepeer-review


We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective measurements using only communication with neighbors in the network. Our distributed approach to this problem is based on the centralized Iterative Hard Thresholding algorithm (IHT). We first present a distributed IHT algorithm for static networks that leverages standard tools from distributed computing to execute in-network computations with minimized bandwidth consumption. Next, we address distributed signal recovery in networks with time-varying topologies. The network dynamics necessarily introduce inaccuracies to our in-network computations. To accommodate these inaccuracies, we show how centralized IHT can be extended to include inexact computations while still providing the same recovery guarantees as the original IHT algorithm. We then leverage these new theoretical results to develop a distributed version of IHT for time-varying networks. Evaluations show that our distributed algorithms for both static and time-varying networks outperform previously proposed solutions in time and bandwidth by several orders of magnitude.

Original languageEnglish
Article number6858033
Pages (from-to)4931-4946
Number of pages16
JournalIEEE Transactions on Signal Processing
Issue number19
StatePublished - 1 Oct 2014


  • Compressed sensing
  • distributed algorithm
  • distributed consensus
  • iterative hard thresholding
  • sparse recovery

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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