English(EN) On the Adversarial Vulnerability of Neural Receivers with Unrolled LDPC Decoders

新研究探讨了5G神经接收器的对抗性攻击

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 04:00

研究人员调查了5G新无线电（NR）系统中使用的神经接收器的对抗性漏洞。他们发现，由于展开的低密度奇偶校验（LDPC）解码器中的双曲正切（tanh）饱和引起的梯度掩蔽，标准的基于梯度的攻击无效。为了解决这个问题，提出了两种新的损失函数：L_BP和L_LLR。L_BP使用每迭代监督，通过未饱和的早期迭代来路由梯度，而L_LLR则对预解码器对数似然比（LLR）应用裕度损失，完全绕过了解码器。这两种方法在对抗性扰动下都显著提高了块错误率（BLER）。 AI

影响这项研究可能带来更强大的AI驱动的通信系统，提高对抗性攻击的安全性。

排序理由该集群包含一篇详细介绍新发现和提出方法的论文。[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 →

本文如何被排名

Signal score: 4 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新发现和提出方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Aakash Saini, Jafar Aco Mohammadi, Wolfgang Gerstacker · 2026-10-23 04:00

On the Adversarial Vulnerability of Neural Receivers with Unrolled LDPC Decoders

arXiv:2610.30345v1 Announce Type: cross Abstract: Neural receivers for 5G New Radio (NR) match classical model-based receivers in link-level performance, but their robustness to adversarial perturbations at the receiver input remains underexplored. We show that standard gradient-…

本文如何被排名

报道来源 [1]

On the Adversarial Vulnerability of Neural Receivers with Unrolled LDPC Decoders

相关实体

相关话题