A Channel Robust RF Fingerprint Identification Scheme for LTE Devices Based on DMRS Signals
Xuan Yang, Dongming Li, Fuhui Zhou, Naofal Al‐Dhahir · 2023
In physical-layer security schemes, radio frequency fingerprint (RFF) identification is vulnerable to the channel variations, and the identification performance of mobile devices using long term evolution (LTE) signals remains to be validated. In this paper, we propose an RFF extraction method based on LTE demodulation reference signal (DMRS) signal processing for LTE mobile devices. First, we analyze the impacts of the RFF and channel fading on DMRS in the LTE uplink channel. Then, we propose an RFF extraction method based on the wavelet decomposition and reconstruction of DMRS. By removing the low-frequency components of DMRS, which are mainly affected by the channel effects, our proposed method is robust to the channel impairments. Finally, our simulation and experimental results show that our method can effectively reduce the channel impacts and retain the RFF of devices. The effectiveness of this method is verified via different classification tasks. The classification accuracy can reach 98.5% and 93.9% in the stationary and mobile scenarios, respectively.