English(EN) Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding

研究人员利用代码自同构改进纠错码的神经解码

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-05 10:48

研究人员开发了一种新方法，用于改进通信系统纠错中的基于综合征的神经解码（SBND）。通过利用代码自同构，该方法通过数据增强增强了SBND模型的泛化能力。该技术允许模型在较小的数据集和适当的训练下达到接近最大似然解码的性能，这表明由于训练不足，以前的结果可能低估了SBND的潜力。 AI

影响引入了一种利用深度学习改进通信系统纠错的新技术，可能提高各种应用中的数据完整性。

排序理由这是一篇发表在arXiv上的研究论文，详细介绍了一种新的神经解码方法。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Rapha\"el Le Bidan, Ahmad Ismail, Elsa Dupraz, Charbel Abdel Nour · 2026-05-06 04:00

利用代码自同构改进基于综合征的神经解码

arXiv:2605.03620v1 Announce Type: cross 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,…
arXiv cs.LG TIER_1 English(EN) · Charbel Abdel Nour · 2026-05-05 10:48

利用代码自同构改进基于综合征的神经解码

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 enh…