Optimal quantization for compressive sensing under message passing reconstruction

Ulugbek Kamilov; Vivek K. Goyal; Sundeep Rangan

doi:10.1109/ISIT.2011.6034168

Optimal quantization for compressive sensing under message passing reconstruction

Ulugbek Kamilov
, Vivek K. Goyal
, Sundeep Rangan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Scopus citations

Abstract

We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers.

Original language	English
Title of host publication	2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
Pages	459-463
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2011.6034168
State	Published - 2011
Event	2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011 - St. Petersburg, Russian Federation Duration: Jul 31 2011 → Aug 5 2011

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
ISSN (Print)	2157-8104

Conference

Conference	2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
Country/Territory	Russian Federation
City	St. Petersburg
Period	07/31/11 → 08/5/11

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10.1109/ISIT.2011.6034168

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title = "Optimal quantization for compressive sensing under message passing reconstruction",

abstract = "We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers.",

author = "Ulugbek Kamilov and Goyal, \{Vivek K.\} and Sundeep Rangan",

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doi = "10.1109/ISIT.2011.6034168",

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series = "IEEE International Symposium on Information Theory - Proceedings",

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Kamilov, U, Goyal, VK & Rangan, S 2011, Optimal quantization for compressive sensing under message passing reconstruction. in 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011., 6034168, IEEE International Symposium on Information Theory - Proceedings, pp. 459-463, 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011, St. Petersburg, Russian Federation, 07/31/11. https://doi.org/10.1109/ISIT.2011.6034168

Optimal quantization for compressive sensing under message passing reconstruction. / Kamilov, Ulugbek; Goyal, Vivek K.; Rangan, Sundeep.
2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011. 2011. p. 459-463 6034168 (IEEE International Symposium on Information Theory - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Optimal quantization for compressive sensing under message passing reconstruction

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