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Learning to Decode Linear Codes with Generalized Belief Propagation Network — arXiv:2611.06874

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.06874
License (as archived): CC0-1.0
Archived: 2026-11-05T18:17:13.741Z
SHA-256 of the archived text: 6c21af59568858e66254b67ff5b4b875172870a10aea8cf68bf8fe6205936faf
Stable URL of this page: https://metraipolis.com/documento/dc_447ff6eee00d

How to cite

Learning to Decode Linear Codes with Generalized Belief Propagation Network — arXiv:2611.06874 — arXiv (en), https://arxiv.org/abs/2611.06874 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_447ff6eee00d (Metraipolis archive dc_447ff6eee00d, sha256 6c21af59568858e66254b67ff5b4b875172870a10aea8cf68bf8fe6205936faf)

BibTeX

@misc{dc447ff6eee00d,
  title = {Learning to Decode Linear Codes with Generalized Belief Propagation Network — arXiv:2611.06874},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.06874},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_447ff6eee00d; sha256 6c21af59568858e66254b67ff5b4b875172870a10aea8cf68bf8fe6205936faf},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.06874. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels. Recently, neural decoders have demonstrated notable performance gains over classical iterative algorithms. In this paper, we propose a Generalized Belief Propagation Network (GBPNet) that preserves message-passing symmetry while enhancing the decoder's learning capability. The architecture uses cascaded variable-node updates and learned element-wise message scaling while retaining the Tanner graph structure. We also introduce two training strategies, progressive training and high-SNR training, that improve decoding accuracy. Experimental results on several linear codes show that GBPNet achieves lower bit error rates than conventional belief propagation and the evaluated state-of-the-art neural decoders.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.06874 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-05T18:12:49.020Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).