Length-Versatile and Few-Shot Radio Frequency Fingerprint Identification Using Unsupervised Self-Distillation

Due to the uniqueness and stability of radio frequency fingerprints (RFF), radio frequency fingerprint identification (RFFI) is an important physical layer authentication method in the security of Internet of Things (IoT). However, existing deep-learning-based RFFI methods require a large number of labeled samples to achieve ideal performance. Besides, when the signal length changes, the network structure needs to be redesigned and the entire training process needs to be reconducted. To address this issue, we propose a few shot RFFI method on the basis of self-distillation with no labels (SDINO). Within it, a network termed DPformer is designed, which can adapt signals of varying lengths and is more lightweight. When the signal length changes, there is no need to retrain the network, and it is more lightweight. Simulation results show that, compared with existing methods, the proposed method achieves better recognition performance and more lightweight on LoRa dataset with 30 classes.