Random Railings Enhancement For RFF Imbalanced Data Augmentation

Abstract

Radio Frequency Fingerprint (RFF) technology is an effective means to defend against cheating and counterfeit attacks in wireless communication. A deep learning-based RFF recognition algorithm can achieve well recognition performance, but it needs many balanced samples to train the model. However, the problem of sample imbalance is widespread in RFF identification tasks, and the number of signal samples of illegal devices is minimal. Neural network models usually can't learn these minority representations well, which seriously affects the performance of RFF recognition. Many advanced algorithms proposed to alleviate the problem of data imbalance don't perform well in the task of RFF recognition because they ignore the characteristics of RFF signals. Therefore, an algorithm based on Random Railings Enhancement (RRE) is proposed in this paper, which fills the data set with random masks according to the signal values front and rear. RRE protects the original signal's information, effectively expands the rare dataset, and has the effect of data enhancement. The experimental results show that the RRE can improve the performance of Radio Frequency (RF) identification technology tasks in the case of imbalanced data sets.