Optimal quantization for compressive sensing under message passing reconstruction
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Goyal_Optimal quantization.pdf
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Author(s)
Kamilov, Ulugbek
Goyal, Vivek K.
Rangan, Sundeep
Date Issued
October 2011
Journal
Proceedings of the IEEE International Symposium on Information Theory Proceedings (ISIT), 2011
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kamilov, Ulugbek, Vivek K Goyal, and Sundeep Rangan. “Optimal Quantization for Compressive Sensing Under Message Passing Reconstruction.” IEEE International Symposium on Information Theory Proceedings (ISIT), 2011. 459–463.
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/ISIT.2011.6034168
