Learned Decimation for Neural Belief Propagation Decoders : Invited Paper

Andreas Buchberger; Christian Häger; Henry D. Pfister; Laurent Schmalen; Alexandre Graell i Amat

ICASSP 2021accepted0 citations

Learned Decimation for Neural Belief Propagation Decoders : Invited Paper

Andreas Buchberger, Christian Häger, Henry D. Pfister, Laurent Schmalen, Alexandre Graell i Amat

Abstract

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> .

BibTeX

@inproceedings{icassp2021_learneddecimatio,
  title = {Learned Decimation for Neural Belief Propagation Decoders : Invited Paper},
  author = {Andreas Buchberger and Christian Häger and Henry D. Pfister and Laurent Schmalen and Alexandre Graell i Amat},
  booktitle = {ICASSP 2021},
  year = {2021}
}