Journal of Terahertz Science and Electronic Information Technology , Volume. 20, Issue 12, 1305(2022)
Convolutional Neural Network Radio Frequency fingerprint identification method for co-existence of cooperative signal and spoofing signal
The radio frequency fingerprints are inherent features of the device hardware, and will not change with the transmitted signal, therefore they are often used in communication anti-spoofing. In this paper, the neural network is adopted to process the original signal samples obtained by the receiver, including I/Q sequence, amplitude/phase, binary image of constellation diagram and color density diagram of constellation diagram to achieve anti-deception effect. When the signal-to-interference and noise ratio is in the range of -30 dB to 30 dB, the signal recognition accuracy can reach up to 99.93%. Being different from the existing literature, the method can be adapted to the scenes with different signalto- interference and noise ratios. This research shows that the proposed method is feasible to achieve anti-spoofing in a complex communication environment where spoofing signals and legal signals coexist.
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ZHANG Yaqi, YANG Chun, LIU Youjiang, YANG Dalong, QIU Yongtao. Convolutional Neural Network Radio Frequency fingerprint identification method for co-existence of cooperative signal and spoofing signal[J]. Journal of Terahertz Science and Electronic Information Technology , 2022, 20(12): 1305
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Received: Oct. 1, 2021
Accepted: --
Published Online: Feb. 17, 2023
The Author Email: Yaqi ZHANG (zhangyaqi19@gscaep.ac.cn)