Compressive sensing over networks
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Medard_Compressive sensing.pdf
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308.59 KB
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Author(s)
Medard, Muriel
Feizi-Khankandi, Soheil
Effros, Michelle
Date Issued
February 2011
Journal
Proceedings of the 48th Annual Allerton Converence on Communication, Control, and Computing (Allerton), 2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Médard, Muriel et al. "Compressive sensing over networks." Proceedings of the 48th Annual Allerton Converence on Communication, Control, and Computing (Allerton), 2010: 1129-1136. © 2010 IEEE.
Version
Final published version
Abstract
In this paper, we demonstrate some applications of compressive sensing over networks. We make a connection between compressive sensing and traditional information theoretic techniques in source coding and channel coding. Our results provide an explicit trade-off between the rate and the decoding complexity. The key difference of compressive sensing and traditional information theoretic approaches is at their decoding side. Although optimal decoders to recover the original signal, compressed by source coding have high complexity, the compressive sensing decoder is a linear or convex optimization. First, we investigate applications of compressive sensing on distributed compression of correlated sources. Here, by using compressive sensing, we propose a compression scheme for a family of correlated sources with a modularized decoder, providing a trade-off between the compression rate and the decoding complexity. We call this scheme Sparse Distributed Compression. We use this compression scheme for a general multicast network with correlated sources. Here, we first decode some of the sources by a network decoding technique and then, we use a compressive sensing decoder to obtain the whole sources. Then, we investigate applications of compressive sensing on channel coding. We propose a coding scheme that combines compressive sensing and random channel coding for a high-SNR point-to-point Gaussian channel. We call this scheme Sparse Channel Coding. We propose a modularized decoder providing a trade-off between the capacity loss and the decoding complexity. At the receiver side, first, we use a compressive sensing decoder on a noisy signal to obtain a noisy estimate of the original signal and then, we apply a traditional channel coding decoder to find the original signal.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ALLERTON.2010.5707037
