Optimal quantization for compressive sensing under message passing reconstruction

Ulugbek Kamilov, Vivek K. Goyal, Sundeep Rangan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publication2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
Pages459-463
Number of pages5
DOIs
StatePublished - 2011
Event2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011 - St. Petersburg, Russian Federation
Duration: Jul 31 2011Aug 5 2011

Other

Other2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
CountryRussian Federation
CitySt. Petersburg
Period7/31/118/5/11

Fingerprint

Compressive Sensing
Message passing
Message Passing
Quantization
Signal Reconstruction
Signal reconstruction
Belief Propagation
Bipartite Graph
Mean Square
Scalar
Generalise
Arbitrary
Demonstrate

ASJC Scopus subject areas

  • Applied Mathematics
  • Modeling and Simulation
  • Theoretical Computer Science
  • Information Systems

Cite this

Kamilov, U., Goyal, V. K., & Rangan, S. (2011). Optimal quantization for compressive sensing under message passing reconstruction. In 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011 (pp. 459-463). [6034168] 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.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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, pp. 459-463, 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011, St. Petersburg, Russian Federation, 7/31/11. https://doi.org/10.1109/ISIT.2011.6034168
Kamilov U, Goyal VK, Rangan S. Optimal quantization for compressive sensing under message passing reconstruction. In 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011. 2011. p. 459-463. 6034168 https://doi.org/10.1109/ISIT.2011.6034168
Kamilov, Ulugbek ; Goyal, Vivek K. ; Rangan, Sundeep. / Optimal quantization for compressive sensing under message passing reconstruction. 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011. 2011. pp. 459-463
@inproceedings{10cc5ee1152d4076bbdf9cbd9bcff378,
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",
year = "2011",
doi = "10.1109/ISIT.2011.6034168",
language = "English (US)",
isbn = "9781457705953",
pages = "459--463",
booktitle = "2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011",

}

TY - GEN

T1 - Optimal quantization for compressive sensing under message passing reconstruction

AU - Kamilov, Ulugbek

AU - Goyal, Vivek K.

AU - Rangan, Sundeep

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80054806044&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054806044&partnerID=8YFLogxK

U2 - 10.1109/ISIT.2011.6034168

DO - 10.1109/ISIT.2011.6034168

M3 - Conference contribution

AN - SCOPUS:80054806044

SN - 9781457705953

SP - 459

EP - 463

BT - 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011

ER -