RFF-TTA: Physical Information-Aware Prototype for Temporally Varying RF Fingerprinting Online Test-Time-Adaptation

In recent years, deep learning(DL)-based RF fingerprint (RFF) recognition has become a promising wireless device verification technique in the Internet of Things (IoT). However, temporal variations in device load status effects as well as channel effects can lead to inconsistent RF fingerprint distributions during the training and testing phases, which causes performance degradation of DL models. To address this problem, we propose the first test-time-adaptation (TTA) approach to improve the domain generalization ability of RFF recognition models. We first analyze the causes of time-varying RFF distribution shifts, such as carrier frequency offset (CFO), and develop a physical impairment-based data augmentation strategy. Based on this, we further propose a physically information-aware prototype to guide the model for TTA.

Our method requires no model retraining or labeled test samples, and is a lightweight, nonparametric solution. Finally, our approach is extensively evaluated using mobile phones with the IEEE 802.11 orthogonal frequency division multiplexing (OFDM) system, which demonstrates that our scheme can effectively improve RFF average recognition performance by about 7.8%.