Partitioned Compressive Sensing with Neighbor-Weighted Decoding

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Partitioned Compressive Sensing with Neighbor-Weighted Decoding

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dc.contributor.author Kung, H.T. T.
dc.contributor.author Tarsa, Stephen John
dc.date.accessioned 2012-11-30T18:43:07Z
dc.date.issued 2011
dc.identifier.citation Kung, H.T. and Stephen J. Tarsa. 2011. Partitioned compressive sensing with neighbor-weighted decoding. In proceedings of Military Communications Conference (MILCOM 2011), Baltimore, MD, November 7-10, 2011. en_US
dc.identifier.isbn 978-1-4673-0079-7 en_US
dc.identifier.issn 2155-7578 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:9972704
dc.description.abstract Compressive sensing has gained momentum in recent years as an exciting new theory in signal processing with several useful applications. It states that signals known to have a sparse representation may be encoded and later reconstructed using a small number of measurements, approximately proportional to the signal s sparsity rather than its size. This paper addresses a critical problem that arises when scaling compressive sensing to signals of large length: that the time required for decoding becomes prohibitively long, and that decoding is not easily parallelized. We describe a method for partitioned compressive sensing, by which we divide a large signal into smaller blocks that may be decoded in parallel. However, since this process requires a signi cant increase in the number of measurements needed for exact signal reconstruction, we focus on mitigating artifacts that arise due to partitioning in approximately reconstructed signals. Given an error-prone partitioned decoding, we use large magnitude components that are detected with highest accuracy to in uence the decoding of neighboring blocks, and call this approach neighbor-weighted decoding. We show that, for applications with a prede ned error threshold, our method can be used in conjunction with partitioned compressive sensing to improve decoding speed, requiring fewer additional measurements than unweighted or locally-weighted decoding. en_US
dc.description.sponsorship Engineering and Applied Sciences en_US
dc.language.iso en_US en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.relation.isversionof doi:10.1109/MILCOM.2011.6127519 en_US
dc.relation.hasversion http://www.eecs.harvard.edu/~htk/publication/2011-milcom-kung-tarsa.pdf en_US
dash.license OAP
dc.subject bismuth en_US
dc.subject compressed sensing en_US
dc.subject decoding en_US
dc.subject finite wordlength effects en_US
dc.subject frequency measurement en_US
dc.subject matching pursuit algorithms en_US
dc.subject silicon en_US
dc.title Partitioned Compressive Sensing with Neighbor-Weighted Decoding en_US
dc.type Monograph or Book en_US
dc.description.version Accepted Manuscript en_US
dc.relation.journal Military Communications Conference (MILCOM 2011) en_US
dash.depositing.author Kung, H.T. T.
dc.date.available 2012-11-30T18:43:07Z

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  • FAS Scholarly Articles [7374]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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