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An application of generalized belief propagation: splitting trapping sets in LDPC codes

Year: 2014

Type: conference-paper

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.

Source: IEEE International Symposium on Information Theory (ISIT)

DOI: 10.1109/isit.2014.6874924

Authors: Jean-Christophe Sibel, Sylvain Reynal, David Declercq

Institutions: Institut National des Sciences Appliquées de Rennes, Institut d'Électronique et des Technologies du numéRique, Equipes Traitement de l'Information et Systèmes, Université de Cergy-Pontoise, École Nationale Supérieure de l'Électronique et de ses Applications

Language: English

Topic: Error Correcting Code Techniques

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2071918243

FWCI: 0.921

Cites: 27

Cited by: 7

Related to: 10