Multi-periodicity dependency Transformer based on spectrum offset for radio frequency fingerprint identification

Radio Frequency Fingerprint Identification (RFFI) has emerged as a pivotal task for reliable device authentication. Despite advancements in RFFI methods, background noise and intentional modulation features result in weak energy and subtle differences in the RFF features. These challenges diminish the capability of RFFI methods in feature representation, complicating the effective identification of device identities. This paper proposes a novel Multi-Periodicity Dependency Transformer (MPDFormer) to address these challenges. The MPDFormer employs a spectrum offset-based periodicity embedding representation to augment the discrepancy of intrinsic features. We delve into the intricacies of the periodicity-dependency attention mechanism, integrating both intra-period and inter-period attention mechanisms. This mechanism facilitates the extraction of both short-range and long-range periodicity-dependency features, accentuating the feature distinction while concurrently attenuating the perturbations caused by background noise and weak-periodicity features. Empirical results demonstrate MPDFormer's superiority over established baseline methods, achieving impressive millisecond-level inference times on the NVIDIA Jetson Orin NX.