Sparse Dictionary Learning and Per-source Filtering for Blind Radio Source Separation

Annan Dong, Osvaldo Simeone, Alexander Haimovich, Jason Dabin

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

Abstract

Radio frequency sources are observed at a fusion center via sensor measurements made over slow unknown 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 accounted by hidden Markov models. 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. To this end, smooth LASSO is integrated with DL, while the forward-backward algorithm is leveraged for PSF. It is shown that the proposed algorithm can enhance the detection performance of the sources.

Original languageEnglish (US)
Title of host publication2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728111513
DOIs
StatePublished - Apr 16 2019
Event53rd Annual Conference on Information Sciences and Systems, CISS 2019 - Baltimore, United States
Duration: Mar 20 2019Mar 22 2019

Publication series

Name2019 53rd Annual Conference on Information Sciences and Systems, CISS 2019

Conference

Conference53rd Annual Conference on Information Sciences and Systems, CISS 2019
CountryUnited States
CityBaltimore
Period3/20/193/22/19

All Science Journal Classification (ASJC) codes

  • Information Systems

Keywords

  • Blind source separation
  • Dictionary learning
  • Intermittent and sparse sources
  • Wireless networks

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