Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

arXiv:2605.03620 · cs.IT, cs.LG, math.IT · Submitted 2026-05-05 · Read on arXiv

cs.IT, cs.LG, math.IT

Submitted: 2026-05-05

Updated: 2026-08-27

Comments: 6 pages, 7 figures, accepted for publication at IEEE GLOBECOM 2026. Code to reproduce all results is available at: https://github.com/lebidan/sbnd

Code: https://github.com/lebidan/sbnd

License: http://creativecommons.org/licenses/by/4.0/

The gist: Syndrome-based neural decoding (SBND) has emerged as a promising deep learning approach for soft-decision decoding of high-rate, short-length codes.

Terminology

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: https://github.com/lebidan/sbnd.

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