NDLI: Compressive sensing of digital sparse signals

Content Provider	IEEE Xplore Digital Library
Author	Keying Wu Xiaoyong Guo
Copyright Year	2011
Description	Author affiliation: Research & Innovation Center, Alcatel-Lucent Shanghai Bell Co., Ltd. Shanghai, P.R.China (Keying Wu; Xiaoyong Guo)
Abstract	This paper discusses compressive sensing with digital sparse signals. The motivation is that most existing sparse signal recovery algorithms, like matching pursuit, convex relaxation and Bayesian framework, do not fully exploit the digital nature of signals when dealing with digital sparse signals, which result in certain performance losses. In this paper, we solve this problem via a permutation-based multi-dimensional sensing matrix and an iterative recovery algorithm with maximum likelihood (ML) local detectors. The sensing matrix considered consists of several sub-matrices, each composed of a random permutation matrix and a block-diagonal matrix. The measurements generated from the same permutation matrix are referred to as a dimension. The block-diagonal matrices allow the use of the low-complexity ML detector in each dimension, which best utilizes the digital nature of signals. The multi-dimensional structure of the sensing matrix enables information exchange between dimensions through an iterative process to achieve a near global-optimal estimation. Numerical results are used to show the rate-distortion performance of the proposed technique. It is shown that it can achieve much better rate-distortion than the existing approaches based on convex relaxation and Bayesian framework with digital source signals.
Starting Page	1488
Ending Page	1492
File Size	1162626
Page Count	5
File Format	PDF
ISBN	9781612842554
ISSN	15253511
e-ISBN	9781612842547
DOI	10.1109/WCNC.2011.5779350
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-28
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Detectors Sparse matrices Compressed sensing Quantization Rate-distortion permutation Compressive sensing distortion iterative detection maximum likelihood
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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