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

Document Type

Conference Proceeding

Publication Date

4-16-2019

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.

Identifier

85065194409 (Scopus)

ISBN

[9781728111513]

Publication Title

2019 53rd Annual Conference on Information Sciences and Systems Ciss 2019

External Full Text Location

https://doi.org/10.1109/CISS.2019.8693055

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