English(EN) PRISM-AMC: Replay-Free Continual Learning for Automatic Modulation Classification

新的持续学习框架解决了无线系统中不断变化的调制类别问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了PRISM-AMC，一个新颖的持续学习框架，用于无线系统中的自动调制分类（AMC）。该方法解决了不断变化的调制类别和信道条件的问题，而无需访问先前观察到的训练数据，从而避免了灾难性遗忘。PRISM-AMC利用梯度投影内存来保留任务相关信息，并在MIGOU和DeepSig 2018.01A数据集上与其他方法相比，表现出显著减少的遗忘。 AI

影响这项研究通过实现无需数据重放的持续学习，有望提高无线通信系统的适应性和效率。

排序理由该集群包含一篇详细介绍新机器学习框架的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Usman Akram, Matthew Graham, Haris Vikalo · 2026-11-05 05:00

PRISM-AMC：无需重放的持续学习用于自动调制分类

arXiv:2611.06066v1 Announce Type: new Abstract: Automatic modulation classification (AMC) is a key component of cognitive wireless systems, enabling spectrum awareness and adaptive communication. Most deep learning approaches to AMC assume a fixed set of modulation classes and ac…

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PRISM-AMC：无需重放的持续学习用于自动调制分类

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