Channel-Robust Radio Frequency Fingerprint Identification for Cellular Uplink LTE Devices

Radio frequency fingerprint identification (RFFI) is a promising authentication mechanism for physical-layer security. In this article, we thoroughly validate the feasibility of using RFFI for cellular long-term evolution (LTE) devices. First, we conduct simulations to examine the subtle impacts of hardware impairments on LTE signals. The simulated results reveal that I/Q imbalance and power amplifier nonlinearity introduce significant distortions within in-band spectrum, forming unique hardware fingerprints. We then leverage the strong channel correlation between adjacent subcarriers and separate the channel-robust radio frequency fingerprints (RFFs) from uplink demodulation reference signal (DMRS) in Msg3. Subsequently, we construct a hybrid feature matrix to serve as input for a shallow long short-term memory (LSTM) network. Due to the more effective channel mitigation strategy, our method outperforms three benchmarks in terms of classification accuracy under cross-scenario testing. Additionally, we explore the impacts of bandwidth configuration on RFFI, and experimental findings demonstrate that LTE terminals will exhibit more distinct RFF when occupying a larger number of physical resource blocks (RBs) during transmission. We also investigate the stability of RFF toward frequency band variations. The results suggest that there will be a significant accuracy loss under training with one band but testing with another, indicating the importance of frequency band-independent feature extraction in practical environments. Lastly, we expose four key implications to pave the way for exploring corresponding solutions. To the best of our knowledge, it is the first performance evaluation of the RFFI system on different frequency bands and with multiple bandwidth configurations.