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MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes — arXiv:2609.00715

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00715
License (as archived): CC0-1.0
Archived: 2026-10-17T06:52:35.086Z
SHA-256 of the archived text: b4e13cac4f046d9022929064b149fd3e719ff0f7ef3f4d3718cf89602eb1ef47
Stable URL of this page: https://metraipolis.com/documento/dc_452b2df71dfc

How to cite

MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes — arXiv:2609.00715 — arXiv (en), https://arxiv.org/abs/2609.00715 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_452b2df71dfc (Metraipolis archive dc_452b2df71dfc, sha256 b4e13cac4f046d9022929064b149fd3e719ff0f7ef3f4d3718cf89602eb1ef47)

BibTeX

@misc{dc452b2df71dfc,
  title = {MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes — arXiv:2609.00715},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00715},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_452b2df71dfc; sha256 b4e13cac4f046d9022929064b149fd3e719ff0f7ef3f4d3718cf89602eb1ef47},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00715. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are usually achieved in idealized settings with perfect feedback. Over the last few years, machine learning-based schemes have been shown to be promising solutions for implementing feedback-based codes, particularly when combined with short-block-length open-loop error correcting codes (ECCs) in a concatenated coding structure. However, existing ML-based feedback schemes remain agnostic to the outer code's structure, potentially misallocating feedback resources on error patterns already correctable by the outer ECC. To address this, we propose MaskCode, a Transformer-based inner feedback code for concatenated coding systems, which explicitly incorporates structural knowledge of the outer linear block code into the inner feedback encoder design via two synergistic mechanisms: 1) a soft syndrome-based input that informs the encoder about potential parity constraint violations, and 2) a code-aware attention mask derived from the Tanner graph. We further show that end-to-end training with a differentiable belief propagation (BP) decoder offers no additional gain, as MaskCode's structure-aware design already internalizes the structural knowledge of the outer code; in fact, backpropagation through the iterative BP decoder introduces gradient explosion, which degrades rather than improves performance. Extensive evaluations on BCH and LDPC outer codes demonstrate that MaskCode consistently outperforms all baselines, achieving up to 1.5 dB SNR gain.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-02 in cs.LG).
Categorie arXiv: Information Theory (cs.IT) ; Machine Learning (cs.LG); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2609.00715 — 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 2026-10-17T06:41:55.904Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).