Multi-Channel CNN-Based Open-Set RF Fingerprint Identification for LTE Devices

Radio frequency fingerprint identification (RFFI) is a promising technique that exploits the transmitter-specific characteristics of the RF chain for identification. Disregarding its massive deployment, long-term evolution (LTE) systems have not fully benefited from RFFI. In this paper, an RFFI technique is designed to authenticate LTE devices. Three segments of the LTE physical layer random access channel (PRACH) preambles are captured, namely the transient-on, transient-off, and modulation parts. The segments are first converted into differential constellation trace figures (DCTFs), and then a specific type of neural network called multi-channel convolutional neural network (MCCNN) is used for identification. Additionally, the protocol is able to be applied for open-set identification, i.e., unknown device detection. Experiments are conducted with ten LTE mobile phones. The results show that the proposed RFFI scheme is robust against location changes. In the known device classification problem, the classification accuracy can reach 98.70% in the line-of-sight (LOS) scenario and 89.40% in the non-line-of-sight (NLOS) scenario. In the open-set unknown device detection problem, the identification equal error rate (EER) and area under the curve (AUC) reach 0.0545 and 0.9817, respectively, among six known devices and four unknown devices.