A Novel B-MAP Proxy for Greedy Sparse Signal Recovery Algorithms

Jeongmin Chae, Song Nam Hong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a novel greedy algorithm to recover a sparse signal from a small number of noisy measurements. In the proposed method, a new support index is identified for each iteration, based on bit-wise maximum a posteriori (B-MAP) detection. This approach is an optimal in the sense of detecting one of the remaining support indices, provided that all the indices during the previous iterations are perfectly recovered. Unfortunately, the exact computation of B-MAP detection is not practical since it requires a heavy marginalization of a highdimensional sparse vector to compute a posteriori probability of each remaining support. Our major contribution is to present a good proxy, named B-MAP proxy, on the a posteriori probability. The proposed proxy is easily evaluated only using vector correlations as in popular orthogonal matching pursuit (OMP) and accurate enough to represent a relative ordering on the probabilities. Via simulations, we demonstrate that the proposed greedy algorithm yields a higher recovery accuracy than the existing benchmark methods as OMP and MAP-OMP, having the same computational complexity.A full version of this paper is accessible at: https://arxiv.org/abs/1910.12512/

Original languageEnglish
Title of host publication2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1409-1413
Number of pages5
ISBN (Electronic)9781728164328
DOIs
StatePublished - 2020 Jun
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
Duration: 2020 Jul 212020 Jul 26

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2020-June
ISSN (Print)2157-8095

Conference

Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
Country/TerritoryUnited States
CityLos Angeles
Period20/07/2120/07/26

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