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English(EN) MaskCode: Mask Transformer for Feedback-Assisted Coding With Linear Block Codes

MaskCode Transformer 利用结构化知识增强反馈编码

研究人员开发了 MaskCode,这是一种新颖的基于 Transformer 的内部反馈码,旨在增强级联编码系统。MaskCode 通过基于软判决的输入和源自 Tanner 图的感知码的注意力掩码,整合了对外部线性分组码的知识。该方法旨在通过关注奇偶校验约束违规来优化反馈分配。评估表明,MaskCode 的性能持续优于现有方法,在使用 BCH 和 LDPC 外部码时可获得高达 1.5 dB 的信噪比增益。 AI

影响 这项研究可能通过利用机器学习,在通信系统中实现更高效的纠错。

排序理由 这是一篇详细介绍反馈辅助编码新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

MaskCode Transformer 利用结构化知识增强反馈编码

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这是一篇详细介绍反馈辅助编码新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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完整方法见我们的编辑标准

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jonggyu Jang, Hongjae Nam, Vishrant Tripathi, David J. Love, Hyun Jong Yang ·

    MaskCode:用于具有线性分组码的反馈辅助编码的掩码 Transformer

    arXiv:2609.00715v1 Announce Type: cross Abstract: Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are usually achieved in idealized settings with perfect feedback. Over the last few ye…