LTE Device Identification Based on RF Fingerprint with Multi-Channel Convolutional Neural Network
Pengcheng Yin, Linning Peng, Junqing Zhang, Ming Liu, Hua Fu, Aiqun Hu
Abstract
Radio frequency fingerprint (RFF) identification technique has drawn great attention to wireless terminal authentication. Long-Term Evolution (LTE) has been widely deployed all over the world. RFF-based LTE terminal identifications can prevent the potential impersonation or denial of service (DoS) attacks in the physical layer. This paper proposes a novel multi-channel convolutional neural network (MCCNN) for LTE terminal identification. Differential constellation trace figure (DCTF) is extracted from the random access preamble of the physical random access channel (PRACH). To the best knowledge of the authors, this is the first work dedicated to RFF-based LTE terminal identification. The proposed scheme is evaluated in the hardware experimental system consisting of the LTE eNodeB implemented on the software-defined radio (SDR) platform and six LTE mobile phones. Experimental results show that the classification accuracy can reach 98.96% at the SNR level of 30 dB with the line-of-sight (LOS) scenarios. Furthermore, long-time evaluations show that the proposed DCTF-MCCNN scheme is robust over time.