Kung, H.Tarsa, Stephen2012-11-302011Kung, 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, 2011.978-1-4673-0079-72155-7578http://nrs.harvard.edu/urn-3:HUL.InstRepos:9972704Compressive 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-USbismuthcompressed sensingdecodingfinite wordlength effectsfrequency measurementmatching pursuit algorithmssiliconMonograph or Book2012-11-3010.1109/MILCOM.2011.6127519