Sufficient and Necessary Conditions of Distributed Compressed Sensing with Prior Information

Wenbo XU  Yupeng CUI  Yun TIAN  Siye WANG  Jiaru LIN  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.9   pp.2013-2020
Publication Date: 2017/09/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.2013
Type of Manuscript: PAPER
Category: General Fundamentals and Boundaries
compressed sensing (CS),  distributed CS,  prior information,  sufficient and necessary conditions,  support,  

Full Text: PDF(1.4MB)
>>Buy this Article

This paper considers the recovery problem of distributed compressed sensing (DCS), where J (J≥2) signals all have sparse common component and sparse innovation components. The decoder attempts to jointly recover each component based on {Mj} random noisy measurements (j=1,…,J) with the prior information on the support probabilities, i.e., the probabilities that the entries in each component are nonzero. We give both the sufficient and necessary conditions on the total number of measurements $sum olimits_{j = 1}^J M_j$ that is needed to recover the support set of each component perfectly. The results show that when the number of signal J increases, the required average number of measurements $sum olimits_{j = 1}^J M_j/J$ decreases. Furthermore, we propose an extension of one existing algorithm for DCS to exploit the prior information, and simulations verify its improved performance.