Cross-Domain Adaptation for RF Fingerprinting Using Prototypical Networks
Cross-Domain Adaptation for RF Fingerprinting Using Prototypical Networks
复制标题
使用原型网络进行射频指纹识别的跨域适应
DOI：
10.1145/3560905.3568100
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Mao, Shiwen
中科院分区：
文献类型：
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作者：
Mackey, Steven;Zhao, Tianya;Wang, Xuyu;Mao, Shiwen
射频（RF）指纹识别是物联网（IoT）应用中用于识别无线设备的硬件功能。在本文中，我们提出了少射学习（FSL）和原型网络（ptn）来创建一个新的模型，该模型可以适应一个新的领域，只有很少的标记示例。所提出的模型可以减轻射频环境变化引起的域漂移。实验结果表明，该方法可以提高射频指纹识别在不同领域的性能。
Radio frequency (RF) fingerprinting is a hardware feature used in Internet of Things (IoT) applications to identify wireless devices. In this paper, we propose few-shot learning (FSL) and prototypical networks (PTNs) to create a new model that can adapt to a new domain with very few labeled examples. The proposed model can mitigate the domain shift caused by changing RF environments. Experimental results show the proposed method can improve the performance of RF fingerprinting over different domains.