Constraint relaxation and annealed belief propagation for binary networks

Yen Chih Chen*, Yu-Ted Su

*Corresponding author for this work

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

1 Scopus citations

Abstract

In this paper, we propose two novel generalized belief propagation (BP) algorithms to improve the convergence behavior of the conventional BP algorithm. By incorporating a dynamic temperature into the free energy formulation, message passing is performed on a dynamic surface of energy cost. The proposed cooling process helps BP converge to a stable fixed point with a lower energy value that leads to better estimations. For decoding turbo-like error correcting codes, we adopt a parametric Gaussian approximation to relax the binary parity check constraints and generalize the conventional binary networks as well. Both the computational complexity and the convergence rate of the proposed annealed BP algorithms are almost the same as those of the conventional BP algorithm. Simulated performance of the proposed algorithms when they are used to decode a low density parity check (LDPC) code and the (23,12) Golay code is presented to validate our proposals.

Original languageEnglish
Title of host publicationProceedings - 2007 IEEE International Symposium on Information Theory, ISIT 2007
Pages321-325
Number of pages5
DOIs
StatePublished - 1 Dec 2007
Event2007 IEEE International Symposium on Information Theory, ISIT 2007 - Nice, France
Duration: 24 Jun 200729 Jun 2007

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8101

Conference

Conference2007 IEEE International Symposium on Information Theory, ISIT 2007
CountryFrance
CityNice
Period24/06/0729/06/07

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