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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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  Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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