Math @ Duke

Publications [#322717] of Henry Pfister
Papers Published
 Lian, M; Pfister, HD, Beliefpropagation reconstruction for compressed sensing: Quantization vs. Gaussian approximation,
2015 53rd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2015
(April, 2016),
pp. 11061113, IEEE, ISBN 9781509018239 [doi]
(last updated on 2019/07/18)
Abstract: © 2015 IEEE. This work considers the compressed sensing (CS) of i.i.d. signals with sparse measurement matrices and beliefpropagation (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.


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