LAUSR

Radio frequency signal identification technology is a non-password authentication method based on the physical layer hardware of the device. This paper explores the feature extraction and recognition method based on the gene characteristics of the radio frequency signal. Since the radio frequency signal has materiality, informationality, transitivity, vulnerability and dividability, these characters are similar to biological gene. The concept of gene characteristics of the radio frequency signal is proposed in the paper, which is inspired by the biological gene. The study on radio frequency signal gene characteristics from the perspective of fractal theory based on the self-similarity of the radio frequency signal is carried out, and the radio frequency signal gene feature extraction is explored by the multifractal dimension characteristics. The experimental results illustrate that the recognition success rate of 500 sets of radio frequency signals from ten identical radio stations can reach 98.2% with the gray relation algorithm as a classifier. It shows that the multifractal dimension characteristics can be used to express the gene features of the radio frequency signals, although the time domain signals from these ten identical radio stations are basically similar. Extending to the cognitive research of radio frequency signals, it can be applied to enhance physical layer security for industrial Internet of Things (IoT) through collaborative identity authentication of industrial IoT wireless devices based on the radio frequency signal gene characteristics.