Differential Complex-Valued Convolutional Neural Network-Based Individual Recognition of Communication Radiation Sources

Abstract

Data assaults from unauthorized access to the Internet of Things will induce severe intrusion and hazard to the whole network. Employing only traditional application layer password authentication approaches cannot guarantee the security of the communication system. Therefore, it is critical to develop a capable and efficient radio frequency fingerprints based physical layer authentication system. To incorporate the domain knowledge in more capable feature extracting and reduce information loss caused by converting RF baseband I/Q signals, we propose a novel differential complex-valued convolutional neural network based individual recognition approach of communication radiation sources in the paper. The proposed method can fully capture the nonlinearity of the RF baseband I/Q signals while decreasing the unfavorable impact of phase rotation induced by carrier frequency offset, which also significantly reduces the required data length of the collected steady-state data transmission section. The recognition performance evaluation on 20 wireless network card devices with duplicate batch, type, and manufacturer shows that the proposed approach has the best recognition performance compared with two conventional approaches whose recognition accuracies are lower than 95%, achieving the total recognition accuracy of 99.7%. Moreover, compared with constellation based approaches, which require at least 5,000 to 10,000 data points as input parameters, the proposed method can reduce the required data length of the collected steady-state data transmission section effectively, which is easier to implement in practical applications.