@inproceedings{e2d17ac3f0d346d0ae9512d136f94477,
title = "Analysis of regularized LS reconstruction and random matrix ensembles in compressed sensing",
abstract = "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.",
author = "Mikko Vehkapera and Yoshiyuki Kabashima and Saikat Chatterjee",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ISIT.2014.6875422",
language = "English",
isbn = "978-1-4799-5186-4",
series = "IEEE International Symposium on Information Theory. Proceedings",
publisher = "IEEE Institute of Electrical and Electronic Engineers",
pages = "3185--3189",
booktitle = "2014 IEEE International Symposium on Information Theory, ISIT 2014",
address = "United States",
note = "2014 IEEE International Symposium on Information Theory, ISIT 2014 ; Conference date: 29-06-2014 Through 04-07-2014",
}