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.

翻译：基于综合征的神经解码(SBND)已成为一种有前景的深度学习方法，用于高速率、短长度码的软判决解码。然而，该方法仍有显著改进空间。本文展示了如何利用码自同构来增强现有SBND模型的学习和泛化能力，通过在训练和推理过程中进行数据扩增。因此，针对所考虑的高速率短码，我们获得了使用小规模数据集和适当训练即可接近最大似然解码性能的模型。我们的研究结果还表明，文献中许多SBND模型的先前结果因训练不足而低估了其真实纠错能力。重现所有结果的代码可在以下网址获取：https://github.com/lebidan/sbnd。