An application of Generalized Belief Propagation: splitting trapping sets in LDPC codes

Abstract : Generalized belief propagation (GBP) is known to be a well-suited technique for approximate inference problems in loopy factor graphs. It can absorb problematic subgraphs inside regions to reduce their influence on the inference. However, the choice of regions to be used in GBP remains a delicate issue. This paper proposes an approach to create specific regions when dealing with Low-Density Parity-Check (LDPC) codes. We split trapping sets, known to degrade the decoding performance, to make GBP locally optimal. Experiments show that GBP can then perform better than BP, especially in the error-floor region.
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2014 IEEE International Symposium on Information Theory (ISIT 2014), Jun 2014, Honolulu, HI, United States. 2014 IEEE International Symposium on Information Theory (ISIT), 〈10.1109/ISIT.2014.6874924〉
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Dernière modification le : mercredi 11 avril 2018 - 15:10:07

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Jean-Christophe Sibel, Sylvain Reynal, David Declercq. An application of Generalized Belief Propagation: splitting trapping sets in LDPC codes. 2014 IEEE International Symposium on Information Theory (ISIT 2014), Jun 2014, Honolulu, HI, United States. 2014 IEEE International Symposium on Information Theory (ISIT), 〈10.1109/ISIT.2014.6874924〉. 〈hal-01680249〉

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