Change search
ReferencesLink to record
Permanent link

Direct link
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 (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.
, 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
URN: urn:nbn:se:kth:diva-167577DOI: 10.1109/ISIT.2014.6875422ISI: 000346496103066ScopusID: 2-s2.0-84906536104ISBN: 9781479951864OAI: diva2:815898
2014 IEEE International Symposium on Information Theory, ISIT 2014, 29 June 2014 through 4 July 2014, Honolulu, HI

QC 20150602

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

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Chatterjee, Saikat
By organisation
Communication TheoryACCESS Linnaeus Centre
Computer Science

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 16 hits
ReferencesLink to record
Permanent link

Direct link