Learned Decimation for Neural Belief Propagation Decoders
Paper i proceeding, 2021

We introduce a two-stage decimation process to improve the performance of neural belief propagation (NBP), recently introduced by Nachmani et al., for short low-density parity-check (LDPC) codes. In the first stage, we build a list by iterating between a conventional NBP decoder and guessing the least reliable bit. The second stage iterates between a conventional NBP decoder and learned decimation, where we use a neural network to decide the decimation value for each bit. For a (128,64) LDPC code, the proposed NBP with decimation outperforms NBP decoding by 0.75dB and performs within 1dB from maximum-likelihood decoding at a block error rate of 10-4.

Författare

Andreas Buchberger

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Christian Häger

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

Henry D. Pfister

Laurent Schmalen

Alexandre Graell I Amat

Chalmers, Elektroteknik, Kommunikations- och antennsystem, Kommunikationssystem

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Invited paper
Toronto, Canada,

Pålitlig och säker kodad kantberäkning

Vetenskapsrådet (VR) (2020-03687), 2021-01-01 -- 2024-12-31.

Styrkeområden

Informations- och kommunikationsteknik

Ämneskategorier

Telekommunikation

Sannolikhetsteori och statistik

Signalbehandling

DOI

10.1109/ICASSP39728.2021.9414407

Mer information

Skapat

2021-08-07