Cross-Domain Adaptation for RF Fingerprinting Using Prototypical Networks
Steven Mackey, Tianya Zhao, Xuyu Wang, Shiwen Mao · 2022
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.