Compressive sensing over networks
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 com...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2012
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| Online Access: | http://hdl.handle.net/1721.1/73605 https://orcid.org/0000-0002-0964-0616 https://orcid.org/0000-0003-4059-407X |
| _version_ | 1811091052598132736 |
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| author | Medard, Muriel Feizi-Khankandi, Soheil Effros, Michelle |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Medard, Muriel Feizi-Khankandi, Soheil Effros, Michelle |
| author_sort | Medard, Muriel |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T14:56:22Z |
| format | Article |
| id | mit-1721.1/73605 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:56:22Z |
| publishDate | 2012 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/736052022-10-01T23:29:16Z Compressive sensing over networks Medard, Muriel Feizi-Khankandi, Soheil Effros, Michelle Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Medard, Muriel Feizi-Khankandi, Soheil 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. United States. Air Force Office of Scientific Research (award 016974-002) 2012-10-04T16:33:25Z 2012-10-04T16:33:25Z 2011-02 2010-09 Article http://purl.org/eprint/type/ConferencePaper 978-1-4244-8215-3 http://hdl.handle.net/1721.1/73605 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. https://orcid.org/0000-0002-0964-0616 https://orcid.org/0000-0003-4059-407X en_US http://dx.doi.org/ 10.1109/ALLERTON.2010.5707037 Proceedings of the 48th Annual Allerton Converence on Communication, Control, and Computing (Allerton), 2010 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. application/pdf Institute of Electrical and Electronics Engineers (IEEE) IEEE |
| spellingShingle | Medard, Muriel Feizi-Khankandi, Soheil Effros, Michelle Compressive sensing over networks |
| title | Compressive sensing over networks |
| title_full | Compressive sensing over networks |
| title_fullStr | Compressive sensing over networks |
| title_full_unstemmed | Compressive sensing over networks |
| title_short | Compressive sensing over networks |
| title_sort | compressive sensing over networks |
| url | http://hdl.handle.net/1721.1/73605 https://orcid.org/0000-0002-0964-0616 https://orcid.org/0000-0003-4059-407X |
| work_keys_str_mv | AT medardmuriel compressivesensingovernetworks AT feizikhankandisoheil compressivesensingovernetworks AT effrosmichelle compressivesensingovernetworks |