Belief-propagation reconstruction for compressed sensing: Quantization vs. Gaussian approximation

Published

Conference Paper

© 2015 IEEE. This work considers the compressed sensing (CS) of i.i.d. signals with sparse measurement matrices and belief-propagation (BP) reconstruction. In general, BP reconstruction for CS requires the passing of messages that are distributions over the real numbers. To implement this in practice, one typically uses either quantized distributions or a Gaussian approximation. In this work, we use density evolution to compare the reconstruction performance of these two methods. Since the reconstruction performance depends on the signal realization, this analysis makes use of a novel change of variables to analyze the performance for a typical signal. Simulation results are provided to support the results.

Full Text

Duke Authors

Cited Authors

  • Lian, M; Pfister, HD

Published Date

  • April 4, 2016

Published In

  • 2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015

Start / End Page

  • 1106 - 1113

International Standard Book Number 13 (ISBN-13)

  • 9781509018239

Digital Object Identifier (DOI)

  • 10.1109/ALLERTON.2015.7447132

Citation Source

  • Scopus