TY - JOUR
T1 - Joint Sparsity Pattern Recovery With 1-b Compressive Sensing in Distributed Sensor Networks
AU - Kafle, Swatantra
AU - Gupta, Vipul
AU - Kailkhura, Bhavya
AU - Wimalajeewa, Thakshila
AU - Varshney, Pramod K.
N1 - Funding Information:
Manuscript received December 9, 2017; revised April 12, 2018; accepted April 29, 2018. Date of publication May 16, 2018; date of current version February 5, 2019. This work was supported in part by ARO under Grant W911NF-14-1-0339; and in part by the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-JRNL-751638. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. A. Dogandzic. (Corresponding author: Swatantra Kafle.) S. Kafle, T. Wimalajeewa, and P. K. Varshney are with the Department of EECS, Syracuse University, Syracuse, NY 13244 USA (e-mail:,skafle@syr.edu; twwewelw@syr.edu; varshney@syr.edu).
Publisher Copyright:
© 2018 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - In this paper, we study the problem of joint sparse support recovery with 1-b quantized compressive measurements in a distributed sensor network. Multiple nodes in the network are assumed to observe sparse signals having the same but unknown sparse support. Each node quantizes its measurement vector element-wise to 1 b. First, we consider that all the quantized measurements are available at a central fusion center. We derive performance bounds for sparsity pattern recovery using 1-bit quantized measurements from multiple sensors when the maximum likelihood decoder is employed. We further develop two computationally tractable algorithms for joint sparse support recovery in the centralized setting. One algorithm minimizes a cost function defined as the sum of the likelihood function and the l 1,∞ quasi-norm, while the other algorithm extends the binary iterative hard thresholding algorithm to the multiple measurement vector case. Second, we consider a decentralized setting where each node transmits 1-b measurements to its one-hop neighbors. The basic idea behind the algorithms developed in the decentralized setting is to embed collaboration among nodes and fusion strategies. We show that even with noisy 1-b compressed measurements, joint support recovery can be carried out accurately in both centralized and decentralized settings. We further show that the performance of the proposed 1-bit compressive sensing-based algorithms is very close to that of their real-valued counterparts except when the signal-to-noise ratio is very small.
AB - In this paper, we study the problem of joint sparse support recovery with 1-b quantized compressive measurements in a distributed sensor network. Multiple nodes in the network are assumed to observe sparse signals having the same but unknown sparse support. Each node quantizes its measurement vector element-wise to 1 b. First, we consider that all the quantized measurements are available at a central fusion center. We derive performance bounds for sparsity pattern recovery using 1-bit quantized measurements from multiple sensors when the maximum likelihood decoder is employed. We further develop two computationally tractable algorithms for joint sparse support recovery in the centralized setting. One algorithm minimizes a cost function defined as the sum of the likelihood function and the l 1,∞ quasi-norm, while the other algorithm extends the binary iterative hard thresholding algorithm to the multiple measurement vector case. Second, we consider a decentralized setting where each node transmits 1-b measurements to its one-hop neighbors. The basic idea behind the algorithms developed in the decentralized setting is to embed collaboration among nodes and fusion strategies. We show that even with noisy 1-b compressed measurements, joint support recovery can be carried out accurately in both centralized and decentralized settings. We further show that the performance of the proposed 1-bit compressive sensing-based algorithms is very close to that of their real-valued counterparts except when the signal-to-noise ratio is very small.
KW - Compressive sensing
KW - binary iterative hard thresholding
KW - common sparsity pattern recovery
KW - distributed sensor networks
KW - quantization
UR - http://www.scopus.com/inward/record.url?scp=85061761423&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061761423&partnerID=8YFLogxK
U2 - 10.1109/TSIPN.2018.2838038
DO - 10.1109/TSIPN.2018.2838038
M3 - Article
AN - SCOPUS:85061761423
SN - 2373-776X
VL - 5
SP - 15
EP - 30
JO - IEEE Transactions on Signal and Information Processing over Networks
JF - IEEE Transactions on Signal and Information Processing over Networks
IS - 1
M1 - 8360527
ER -