Name
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 sub-Gaussian 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISIT.2009.5205809 | international symposium on information theory |
Keywords | DocType | Volume |
gaussian case,achievable result,compound channel,asymptotic performance,sparse signal recovery,existing result,sub-linear regime,linear sparsity regime,information theory,measurement ensemble,converse idea,indexes,signal processing,data compression,measurement uncertainty,compressed sensing,gaussian processes,decoding,noise measurement | Conference | abs/0904.4525 |
ISBN | Citations | PageRank |
978-1-4244-4313-0 | 9 | 0.94 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Tune | 1 | 83 | 8.83 |
| Sibi Raj Bhaskaran | 2 | 28 | 2.89 |
| Stephen Hanly | 3 | 13 | 1.99 |