Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

Abstract:Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes. However, this approach still has substantial room for improvement. In this paper, we show how to leverage code automorphisms to enhance the ability of existing SBND models to learn and generalize through data augmentation during training and inference. As a result, for the short high-rate codes considered, we obtain models that closely approach MLD performance using small datasets and proper training. Our findings also suggest that many prior results for SBND models in the literature underestimate their true correction capability due to undertraining. Code to reproduce all results is available at: this https URL.

6 pages, 7 figures, accepted for publication at IEEE GLOBECOM 2026. Code to reproduce all results is available at: this https URL

Information Theory (cs.IT); Machine Learning (cs.LG)

arXiv:2605.03620 [cs.IT]

(or arXiv:2605.03620v2 [cs.IT] for this version)

https://doi.org/10.48550/arXiv.2605.03620 Focus to learn more arXiv-issued DOI via DataCite

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