利用编码自同构提升神经译码模型性能，实现小数据下的高精度解码。
Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding
- 通过代码自同构生成训练数据，增强模型泛化能力。
- 在短码高率场景下逼近最大似然译码性能，仅需小规模数据集。
- 揭示以往研究低估了神经译码模型的真实纠错能力。
基于校验子的神经译码（SBND）已成为高率、短码软判决译码的有前景深度学习方法。然而该方法仍有较大改进空间。本文展示如何利用代码自同构，在训练和推理阶段通过数据增强提升现有SBND模型的学习与泛化能力。结果表明，针对所考虑的短码高率场景，使用小数据集和合理训练策略，即可获得接近最大似然译码（MLD）性能的模型。研究还指出，文献中许多先前的SBND结果因训练不足而低估了其真实纠错能力。所有实验代码已开源：https://github.com/lebidan/sbnd。
原文摘要 · Abstract (English)
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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