Number of measurements in sparse signal recovery

Paul Tune; Sibi Raj Bhaskaran; Stephen Hanly

doi:10.1109/ISIT.2009.5205809

Number of measurements in sparse signal recovery

Paul Tune^*, Sibi Raj Bhaskaran, Stephen Hanly

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

11 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2009 IEEE International Symposium on Information Theory, ISIT 2009
Place of Publication	Piscataway, N.J.
Pages	16-20
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2009.5205809
Publication status	Published - 2009
Externally published	Yes
Event	2009 IEEE International Symposium on Information Theory, ISIT 2009 - Seoul, Korea, Republic of Duration: 28 Jun 2009 → 3 Jul 2009

Other

Other	2009 IEEE International Symposium on Information Theory, ISIT 2009
Country	Korea, Republic of
City	Seoul
Period	28/06/09 → 3/07/09

Access to Document

10.1109/ISIT.2009.5205809

Link to publication in Scopus

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abstract = "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.",

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AB - 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.

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