Analysis of regularized LS reconstruction and random matrix ensembles in compressed sensing
2014 (English)Conference paper (Refereed)
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
Information theory, Least-squares estimation, Measurement matrix, Numerical experiments, Random-matrix ensembles, Replica method, Sub-optimal choices, Signal reconstruction
IdentifiersURN: urn:nbn:se:kth:diva-167577DOI: 10.1109/ISIT.2014.6875422ISI: 000346496103066ScopusID: 2-s2.0-84906536104ISBN: 9781479951864OAI: oai:DiVA.org:kth-167577DiVA: diva2:815898
2014 IEEE International Symposium on Information Theory, ISIT 2014, 29 June 2014 through 4 July 2014, Honolulu, HI
QC 201506022015-06-022015-05-222015-06-02Bibliographically approved