Transmitter Identification With Contrastive Learning in Incremental Open-Set Recognition

Radio frequency fingerprints are commonly exploited as a unique signature in the physical layer for distinguishing transmitters in Transmitter Identification Systems (TIS). In response to the growing demand for TIS in open dynamic scenarios, this paper proposes the Incremental Open-Set Recognition (IOSR) framework to address IOSR tasks, which involve changes in transmitter categories, component replacements, and open-set recognition. To overcome the limitations of traditional methods, the proposed framework focuses on enhancing the security, adaptability, reliability, and efficiency of TIS. Specifically, a well-designed data representation and a lightweight extractor based on supervised contrastive learning are introduced to improve inter-class discriminative ability and intra-class compactness for feature extraction. The incorporation of MobileNetV3 reduces the training parameters of the extractor while improving computational efficiency. Moreover, an adaptive evolved block is designed to mitigate catastrophic forgetting in incremental learning, preserving historical knowledge and enhancing system scalability and adaptability. Additionally, an enhanced open-set recognizer is proposed to establish a suitable open-set decision boundary through output calibration. The performance evaluation of the framework on the WiFi dataset showcases its superiority over existing approaches in the closed-set recognition, achieving an accuracy of over 99.6%. It also performs well in incremental tasks, with an accuracy exceeding 98.9%. In the open-set recognition, the framework achieves an accuracy improvement of approximately 8%. Moreover, it demonstrates superior accuracy in the IOSR task, outperforming other algorithms by more than 5.8%. Furthermore, ablation experiments provide further evidence of the effectiveness of the proposed framework, while a complexity comparison demonstrates its ability to balance computational load and accuracy.