TY - JOUR
T1 - Blind Sparse Estimation of Intermittent Sources over Unknown Fading Channels
AU - Dong, Annan
AU - Simeone, Osvaldo
AU - Haimovich, Alexander M.
AU - Dabin, Jason A.
N1 - Funding Information:
Manuscript received March 1, 2019; accepted July 16, 2019. Date of publication August 8, 2019; date of current version October 18, 2019. The work of O. Simeone was supported by the European Research Council under the European Unions Horizon 2020 research and innovation programme under Grant 725731. The work of A. M. Haimovich was supported by the Booz Allen Hamilton Inc. under agreement 12-D-7248 TO 0046. The review of this paper was coordinated by Prof. G. Gui. (Corresponding author: Annan Dong.) A. Dong and A. M. Haimovich are with the CWiP, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: ad372@njit.edu; haimovich@njit.edu).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - Blind source separation
KW - dictionary learning
KW - hidden Markov model
KW - intermittent and sparse sources
KW - wireless networks
UR - http://www.scopus.com/inward/record.url?scp=85073871094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073871094&partnerID=8YFLogxK
U2 - 10.1109/TVT.2019.2933996
DO - 10.1109/TVT.2019.2933996
M3 - Article
AN - SCOPUS:85073871094
SN - 0018-9545
VL - 68
SP - 9861
EP - 9871
JO - IEEE Transactions on Vehicular Communications
JF - IEEE Transactions on Vehicular Communications
IS - 10
M1 - 8792096
ER -