Redistillation of Radio Frequency Knowledge for RFF Imbalanced Sample Recognition

Radio Frequency Fingerprint (RFF) technology is an effective means to defend against cheating and counterfeiting attacks in wireless communication. However, to move from a theoretical algorithm to a practical application, the challenges of imbalanced data samples and environmental noise must be addressed for Radio Frequency (RF) identification technology. Although noise reduction can restore the signal to some extent, the recognition performance of RFF technology is affected when the dataset is imbalanced. While many RF identification algorithms focus on identification performance under a low Signal-to-Noise Ratio (SNR), performance degradation caused by data imbalance is a pressing problem that requires attention. Directly applying re-sampling algorithms in imbalanced dataset processing can lead to data overlap and neural network over-fitting. To address these issues, this paper proposes a “Redistillation of Radio Frequency Knowledge” (RRFK) algorithm combined with knowledge distillation (KD). The experimental results show that the proposed algorithm can achieve good recognition performance in both stepped and long-tail imbalanced data sets.