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Analysis of regularized LS reconstruction and random matrix ensembles in compressed sensing
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2638-6047
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Performance of regularized least-squares estimation in noisy compressed sensing is studied in the limit when the problem dimensions grow large. The sensing matrix is sampled from the rotationally invariant ensemble that encloses as special cases the standard IID and row-orthogonal constructions. The analysis is carried out using the replica method in conjunction with some novel matrix integration results. The numerical experiments show that for noisy compressed sensing, the standard IID ensemble is a suboptimal choice for the measurement matrix. Orthogonal constructions provide a superior performance in all considered scenarios and are easier to implement in practice.

Place, publisher, year, edition, pages
2014. 3185-3189 p.
Series
IEEE International Symposium on Information Theory - Proceedings, ISSN 2157-8095 ; 6875422
Keyword [en]
Information theory, Least-squares estimation, Measurement matrix, Numerical experiments, Random-matrix ensembles, Replica method, Sub-optimal choices, Signal reconstruction
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-167577DOI: 10.1109/ISIT.2014.6875422ISI: 000346496103066Scopus ID: 2-s2.0-84906536104ISBN: 9781479951864 (print)OAI: oai:DiVA.org:kth-167577DiVA: diva2:815898
Conference
2014 IEEE International Symposium on Information Theory, ISIT 2014, 29 June 2014 through 4 July 2014, Honolulu, HI
Note

QC 20150602

Available from: 2015-06-02 Created: 2015-05-22 Last updated: 2015-06-02Bibliographically approved

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Chatterjee, Saikat

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CiteExportLink to record
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Citation style
  • apa
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  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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