NDLI: On joint recovery of sparse signals with common supports

Content Provider	IEEE Xplore Digital Library
Author	Xiaochen Zhao Wei Dai
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK (Xiaochen Zhao; Wei Dai)
Abstract	This work is motivated by a distributed compressed sensing (DCS) scenario where multiple sensors independently perform compressed sensing and the sparse signals share a common support. The heterogeneous case is considered where the numbers of measurements and the noise levels at different sensors may be different. To analyse the performance, we focus on a probability model for sparse signals and use the state evolution tool developed for the approximate message passing (AMP) technique. In the noise free case, we are able to quantify the asymptotic rate region for exact recovery. The rate region has a shape that is significantly different from that by information theoretic analysis and provides a guidance for resource allocation in practice. It shows that an equal allocation of the number of measurements across sensors is strictly suboptimal. Finally, we also study the effect of the correlation among nonzero components from different sparse signals, which appears in many practical scenarios.
Starting Page	541
Ending Page	545
File Size	148945
Page Count	5
File Format	PDF
ISSN	21578117
e-ISBN	9781467377041
DOI	10.1109/ISIT.2015.7282513
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensors Decoding Joints Compressed sensing Noise Algorithm design and analysis Noise measurement
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Subspace penalized sparse learning for joint sparse recovery

Power-efficient joint compressed sensing of multi-lead ECG signals

Analysis and design of irregular graphs for node-based verification-based recovery algorithms in compressed sensing

Performance bounds of compressed sensing recovery algorithms for sparse noisy signals

Joint-sparse recovery in compressed sensing with dictionary mismatch

Recover algorithm of distributed compressed sensing based on FOCUSS

On recovery of sparse signals with block structures

Efficient recovery of block sparse signals via zero-point attracting projection

Thresholding-based reconstruction of compressed correlated signals

On joint recovery of sparse signals with common supports

Similar Documents

Subspace penalized sparse learning for joint sparse recovery

Power-efficient joint compressed sensing of multi-lead ECG signals

Analysis and design of irregular graphs for node-based verification-based recovery algorithms in compressed sensing

Performance bounds of compressed sensing recovery algorithms for sparse noisy signals

Joint-sparse recovery in compressed sensing with dictionary mismatch

Recover algorithm of distributed compressed sensing based on FOCUSS

On recovery of sparse signals with block structures

Efficient recovery of block sparse signals via zero-point attracting projection

Thresholding-based reconstruction of compressed correlated signals

On joint recovery of sparse signals with common supports