Sets of complementary LLRs to improve OSD post-processing of BP decoding - Archive ouverte HAL
Communication Dans Un Congrès Année : 2023

Sets of complementary LLRs to improve OSD post-processing of BP decoding

Résumé

This article deals with Ordered Statistics Decoding (OSD) applied to the soft outputs of the Belief Propagation (BP) algorithm. We first model the weighted sum of the a posteriori LLRs across BP decoding iterations into a neuron. The neuron is then trained with the focal loss to compute for each BP decoding failure a set of accumulated Log Likelihood Ratios (LLRs) suited for OSD post-processing. Then, we propose a recursive selection procedure of LLRs sets, for multiple OSD post-processing. This selection is carried out from the sets of a posteriori LLRs calculated at each BP iteration, and from the accumulated LLRs optimized for the OSD, based on their joint probabilities of failure with OSD post-processing. An OSD is then applied to each set of LLRs belonging to the selection. In addition, we propose to reduce the OSD post-processing decoding complexity without significantly degrading its performance. Our results show that this new decoding method provides an effective way to bridge the gap to maximum likelihood decoding for short and long Low Density Parity Check (LDPC) codes.
Fichier principal
Vignette du fichier
Sets_of_complementary_LLRs_to_improve_OSD_post_processing_of_BP_decoding.pdf (291.38 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04214634 , version 1 (22-09-2023)

Identifiants

  • HAL Id : hal-04214634 , version 1

Citer

Joachim Rosseel, Valérian Mannoni, Valentin Savin, Inbar Fijalkow. Sets of complementary LLRs to improve OSD post-processing of BP decoding. 2023 12th International Symposium on Topics in Coding (ISTC), Sep 2023, Brest, France. ⟨hal-04214634⟩
47 Consultations
92 Téléchargements

Partager

More