Signal-independent RFF Identification for LTE Mobile Devices via Ensemble Deep Learning
Yanjin Qiu, Linning Peng, Junqing Zhang, Ming Liu, Hua Fu, Aiqun Hu · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
Radio frequency fingerprint (RFF)-based wireless device authentication is an emerging technique to prevent potential spoofing attacks in wireless communications. The random access preamble of the physical random access channel (PRACH) in Long Term Evolution (LTE) systems is the first message sent from a user equipment (UE). However, PRACH preambles change under different evolved Node B (eNB), which will affect the RFF extraction. In this paper, a signal-independent RFF extraction method is first proposed to extract varying LTE PRACH preambles under different LTE eNBs. Residual transient segment (RTS) features from the varying PRACH preambles are extracted for RFF identification. A convolutional neural network (CNN) based ensemble deep learning scheme is proposed to integrate benefits from different RFF features. An experimental system under real operator LTE eNB is designed to capture and identify real UE signals. Experimental results show that the classification accuracy of five UEs can reach more than 95% under the same eNB and 85% under different eNBs. Furthermore, longtime evaluations show that the UE RTS feature is robust over time.