Radio Frequency Fingerprint Recognition Method Based on Generative Adversarial Net

Abstract

RF fingerprint recognition is an emerging technology for identifying specific hardware features of wireless transmitters. In order to solve the problem of illegal transmitter hazardous wireless communication security, this paper proposes a method of generating RF fingerprint recognition methods based on generative adversarial net (GAN).This article first uses I / Q data through wavelet transform data pre-processing , Since the wavelet transform can describe the features of different frequency signals, the characteristics of radio frequency fingerprint can be highlighted after wavelet transform of I / Q data. Then we have designed a generative adversarial net model, which consists of a generate model and a discriminant model. Generate model input noise to generate a pseudo data distribution, The discriminant model enters the data distribution of the real trusted transmitter and the generated dummy data distribution generated, and the determination result is fed back to the generator, allowing the generator to update to generate more real pseudo data distributions, better The network model is used for radio frequency fingerprint recognition. Based on the above, it is possible to effectively identify rogue radio frequency transmitters to some extent to solve wireless security issues. The experimental results show that the generative adversarial net (GAN) can distinguish between 98.1% accuracy of the credibility transmitter and illegal transmitter, it has higher accuracy than traditional convolutional neural networks (CNN) and full connectivity neural network (DNN).