Blind Sparse Estimation of Intermittent Sources over Unknown Fading Channels

Annan Dong, Osvaldo Simeone, Alexander M. Haimovich, Jason A. Dabin

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on the output produced by the former. Both stages are designed so as to account for the sparsity and memory of the sources. To this end, smooth LASSO is integrated with DL, while the forward-backward algorithm and Expectation Maximization (EM) algorithm are leveraged for PSF. It is shown that the proposed algorithm can enhance the detection and the estimation performance of the sources, and that it is robust to the sparsity level.

Original languageEnglish (US)
Article number8792096
Pages (from-to)9861-9871
Number of pages11
JournalIEEE Transactions on Vehicular Technology
Issue number10
StatePublished - Oct 2019

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • Blind source separation
  • dictionary learning
  • hidden Markov model
  • intermittent and sparse sources
  • wireless networks


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