Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

ArXi:2605.03620v1 Announce Type: cross 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 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