Code-aware quantizer design for finite-precision min-sum decoders

Abstract : Classically, the quantization of the soft information supplied to a finite-precision decoder is chosen to optimize a certain criterion which does not depend on the characteristics of the existing code. This work studies code-aware quantizers, for finite-precision min-sum decoders, which optimize the noise threshold of the existing family of Low-Density Parity-Check (LDPC) codes. We propose a code-aware quantizer with lower complexity than that obtained by optimizing all decision levels and approaching its performance, for few quantization bits. We show that code-aware quantizers outperform code-independent quantizers in terms of noise threshold for both regular and irregular LDPC codes. To overcome the error floor behavior of LDPC codes, we propose the design of the quantizer for a target error probability at the decoder output. The results show that the quantizer optimized to get a zero error probability could lead to a very bad performance for practical range of signal to noise ratios. Finally, we propose to design jointly irregular LDPC codes and code-aware quantizers for finite-precision min-sum decoders. We show that they achieve significant decoding gains with respect to LDPC codes designed for infinite-precision belief propagation decoding, but decoded by finite-precision min-sum.
Type de document :
Communication dans un congrès
Black Sea Conference on Communications and Networking (BlackSeaCom), 2016 IEEE International, Jun 2016, Varna, Bulgaria. pp.1 - 5, 2016, <10.1109/BlackSeaCom.2016.7901540>
Liste complète des métadonnées


https://hal-cea.archives-ouvertes.fr/cea-01573431
Contributeur : Raffaele D'Errico <>
Soumis le : mercredi 9 août 2017 - 15:16:49
Dernière modification le : vendredi 25 août 2017 - 12:06:46

Fichier

2016-BlackSeaCom_Code-Aware Qu...
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Zeina Mheich, Thien-Truong Nguyen-Ly, Valentin Savin, David Declercq. Code-aware quantizer design for finite-precision min-sum decoders. Black Sea Conference on Communications and Networking (BlackSeaCom), 2016 IEEE International, Jun 2016, Varna, Bulgaria. pp.1 - 5, 2016, <10.1109/BlackSeaCom.2016.7901540>. <cea-01573431>

Partager

Métriques

Consultations de
la notice

85

Téléchargements du document

11