Conventional Neural Network-Based Radio Frequency Fingerprint Identification Using Raw I/Q Data. (22nd August 2022)

• Record Type: Journal Article
• Authors: Yang, Tian; Hu, Su; Wu, Weiwei; Niu, Lixin; Lin, Di; Song, Jiabei
• Other Names: Liu Mingqian Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-08-22
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/8681599
• Languages: English
• ISSNs: 1530-8669

Abstract

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.