Receiver-Agnostic Radio Frequency Fingerprint Identification Based on Disentangled Feature Cross Combination

Radio Frequency Fingerprint Identification (RFFI) has inspired numerous device authentication methods. However, the impact of receiver impairments on transmitter fingerprinting has not been adequately addressed in the existing literature. This letter proposes a receiver-agnostic RFFI model aimed at effectively extracting transmitter-related features from raw signals in the presence of receiver-related interference. Specifically, we employ distinct classifiers to initially extract transmitter-related and receiver-related features. The disentangled features from different samples are then cross-combined and used to obtain a transmitter classifier that is unaffected by receiver-related information. Additionally, a binary mask disentangler and a reconstruction module are incorporated to further extract transmitter-related information from the original samples. Experiments conducted on a real-world dataset demonstrate that our method outperforms four other comparison methods when processing data from unknown receivers.