Allerton 2015 Paper Abstract


Paper ThC1.1

Pfister, Henry D. (Duke University), Lian, Mengke (Duke University)

Belief-Propagation Reconstruction for Compressed Sensing: Quantization vs. Gaussian Approximation

Scheduled for presentation during the Invited Session "Interdisciplinary Statistical Physics" (ThC1), Thursday, October 1, 2015, 13:30−13:50, Library

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Sparse Data Analysis, Source Coding and Compression, Statistical Signal Processing


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.



All Content © PaperCept, Inc..

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2021 PaperCept, Inc.
Page generated 2021-12-05  09:13:32 PST  Terms of use