Compressive Sensing Using Low Density Frames

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dc.creatorAkçakaya, Mehmet
dc.creatorPark, Jinsoo
dc.creatorTarokh, Vahid
dc.date2009-03-03
dc.date.accessioned2026-07-07T12:48:57Z
dc.date.available2026-07-07T12:48:57Z
dc.descriptionWe consider the compressive sensing of a sparse or compressible signal ${\bf x} \in {\mathbb R}^M$. We explicitly construct a class of measurement matrices, referred to as the low density frames, and develop decoding algorithms that produce an accurate estimate $\hat{\bf x}$ even in the presence of additive noise. Low density frames are sparse matrices and have small storage requirements. Our decoding algorithms for these frames have $O(M)$ complexity. Simulation results are provided, demonstrating that our approach significantly outperforms state-of-the-art recovery algorithms for numerous cases of interest. In particular, for Gaussian sparse signals and Gaussian noise, we are within 2 dB range of the theoretical lower bound in most cases.
dc.description11 pages, 6 figures, Submitted to IEEE Transactions on Signal Processing
dc.identifierhttps://arxiv.org/abs/0903.0650
dc.identifierhttp://arxiv.org/abs/0903.0650
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/222238
dc.subjectInformation Theory
dc.subjectComputation
dc.titleCompressive Sensing Using Low Density Frames
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