Conventional Neural Network-Based Radio Frequency Fingerprint Identification Using Raw I/Q Data

Abstract

Radio frequency (RF) fingerprint identification is a nonpassword authentication method based on the physical layer of communication devices. Deep learning methods have thrown new light on RF fingerprint identification. In this paper, a conventional neural network- (CNN-) based RF identification model is proposed. The CNN models are designed to be lightweight. Raw data that reflects the characteristics of the$I$channel, the$Q$channel, and the 2-dimensional$I+Q$data is successively fed into a CNN model. Therefore, three submodels are generated. The final predictive labels are determined by the results of the three submodels through a voting scheme. Experimental results have demonstrated that in the SNR setting at 5 dB, the final recognition accuracy of four transmit devices could achieve as high as 97.25%, while the identification accuracies based on the$I$channel data,$Q$channel data, and$I+Q$channel data are 94.5%, 95%, and 94.5%, respectively. The training time for the 4 devices is around 30 seconds.