Open-Set Domain Adaptive RF Fingerprint Identification Based on Prototype Calibration

Zhen Hong, Jiahao Lin, Taotao Li, Jinhao Wan, Jie Su, Zhenyu Wen

Abstract: As a fundamental security mechanism for wireless Internet of Things (IoT) devices, Radio Frequency Fingerprinting Identification (RFFI) faces significant challenges in open environments and cross-domain deployments. Among existing deep learning-based open-set recognition (OSR) methods, prototype-based approaches are widely adopted. However, these methods often suffer from prototype drift and feature misalignment between source and target domains due to channel effects, device load variations, and other factors. To address these challenges, this letter proposes a Prototype Calibration Domain Adaptation Framework guided by Pseudo-labeling and Domain-Adversarial Neural Networks (DANN), termed PCPD. The framework first leverages a prototype network to learn discriminative representations for each class in the source domain. Subsequently, it employs pseudo-label loss and a domain discriminator to dynamically calibrate domain-shifted prototype points, enhancing feature alignment across domains. Based on the calibrated prototypes, the method significantly improves OSR performance in cross-domain scenarios. Experimental results demonstrate that PCPD outperforms state-of-the-art algorithms, achieving an approximately 10.8% improvement in OSR accuracy under cross-domain conditions.

External IDs:dblp:journals/wcl/HongLLWSW26