LED-RFF: LTE DMRS-Based Channel Robust Radio Frequency Fingerprint Identification Scheme

In physical-layer security schemes, radio frequency fingerprint (RFF) identification is vulnerable to the channel variations, and the identification of the long term evolution (LTE) mobile devices deserves further investigation. In this paper, we propose an RFF extraction method based on the LTE demodulation reference signal (DMRS) processing which can mitigate the channel impairments during the RFF extraction process. First, we analyze the impacts of RFF and the multipath channel on the DMRS in the LTE physical uplink shared channel and take the flexible time-frequency resource block allocations into account. Then, we propose an RFF extraction method based on the wavelet decomposition and reconstruction of the DMRS signals. It is found that the in-phase/quadrature (IQ) direct current offset and the frequency-dependent IQ imbalance mainly affect high-frequency components of the frequency domain DMRS signals, while the power amplifier memory nonlinearity and the channel effects mainly influence the low-frequency components. By removing the low-frequency components of the frequency domain DMRS signals, the proposed method is robust to the channel impairments. Finally, the simulation and experimental results show that our method can effectively reduce the channel impacts and retain the device RFF. 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. Meanwhile, by combining the training sets collected in the static and the moving scenarios together in the training process, the model can achieve better classification performance.