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

Publication series

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

Other

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

ASJC Scopus subject areas

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

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