CaltechAUTHORS
  A Caltech Library Service

Compressive sensing over networks

Feizi, Soheil and Médard, Muriel and Effros, Michelle (2010) Compressive sensing over networks. In: 48th Annual Allerton Conference on Communication, Control, and Computing. IEEE , Piscataway, NJ, pp. 1129-1136. ISBN 978-1-4244-8216-0. http://resolver.caltech.edu/CaltechAUTHORS:20170315-174808247

[img] PDF - Published Version
See Usage Policy.

308Kb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20170315-174808247

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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/ALLERTON.2010.5707037DOIArticle
http://ieeexplore.ieee.org/document/5707037/PublisherArticle
https://arxiv.org/abs/1012.0955arXivDiscussion Paper
Additional Information:© 2010 IEEE. This material is based upon work under subcontract 18870740-37362-C, ITMANET project and award No. 016974-002 supported by AFSOR.
Funders:
Funding AgencyGrant Number
Stanford University18870740-37362-C
Air Force Office of Scientific Research (AFOSR)016974-002
Record Number:CaltechAUTHORS:20170315-174808247
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170315-174808247
Official Citation:S. Feizi, M. Médard and M. Effros, "Compressive sensing over networks," 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Allerton, IL, 2010, pp. 1129-1136. doi: 10.1109/ALLERTON.2010.5707037
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:75168
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:16 Mar 2017 16:31
Last Modified:16 Mar 2017 16:31

Repository Staff Only: item control page