Computer Science – Information Theory
Scientific paper
2009-04-29
Computer Science
Information Theory
6 pages, 1 figure. Extended from conference version with proofs included
Scientific paper
We analyze the asymptotic performance of sparse signal recovery from noisy measurements. In particular, we generalize some of the existing results for the Gaussian case to subgaussian and other ensembles. An achievable result is presented for the linear sparsity regime. A converse on the number of required measurements in the sub-linear regime is also presented, which cover many of the widely used measurement ensembles. Our converse idea makes use of a correspondence between compressed sensing ideas and compound channels in information theory.
Hanly Stephen
Pillai Sibiraj Bhaskaran
Tune Paul
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