Channel-Robust Radio Frequency Fingerprint Identification for LTE Devices with Hybrid Feature

Radio frequency fingerprint (RFF) identification as a physical layer authentication technique by leveraging devices’ unique hardware-level imperfections in transmitted signals has been considered as a potential complement to combat spoofing attacks. In this paper, we develop a lightweight framework to identify long-term evolution (LTE) devices with intrinsic features extracted from Msg3. Channel-robust fingerprints from channel state information (CSI) in demodulation reference signal (DMRS) associated with physical uplink shared channel (PUSCH) are obtained. These modulation features are combined with transient-on features that are obtained from the cyclic prefix (CP). A hybrid feature matrix is constructed and fed into a shallow long short-term memory (LSTM) network, which improves the identification accuracy compared to using single feature representation. We carry out extensive experiments with five LTE devices in real-world environment via a pseudo base station. The robustness of our proposed scheme is evaluated by cross-scenario training and testing. Thanks to the hybrid information-rich feature matrix as input of network, the classification accuracy 90.90% obtained at 25 dB when training in static but testing in dynamic scenarios demonstrates that our scheme is channel-robust in the presence of channel variations.