Title
Sparse Dictionary Learning and Per-source Filtering for Blind Radio Source Separation
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.
Year
DOI
Venue
2019
10.1109/CISS.2019.8693055
2019 53rd Annual Conference on Information Sciences and Systems (CISS)
Keywords
Field
DocType
Blind source separation,Wireless networks,Dictionary learning,Intermittent and sparse sources
Mathematical optimization,Computer science,Lasso (statistics),Filter (signal processing),Communication channel,Algorithm,Fusion center,Hidden Markov model,Blind signal separation,Source separation,Channel state information
Conference
ISBN
Citations 
PageRank 
978-1-7281-1151-3
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Annan Dong100.68
Osvaldo Simeone23574264.99
Alexander M. Haimovich361869.28
Jason Dabin400.34