Convolutional Neural Network Radio Frequency fingerprint identification method for co-existence of cooperative signal and spoofing signal

ZHANG Yaqi; YANG Chun; LIU Youjiang; YANG Dalong; QIU Yongtao

doi:10.11805/tkyda2021356

Journals >Journal of Terahertz Science and Electronic Information Technology >Volume 20 >Issue 12 >Page 1305 > Article

Journal of Terahertz Science and Electronic Information Technology
Vol. 20, Issue 12, 1305 (2022)

Convolutional Neural Network Radio Frequency fingerprint identification method for co-existence of cooperative signal and spoofing signal

ZHANG Yaqi^*, YANG Chun, LIU Youjiang, YANG Dalong, and QIU Yongtao

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[in Chinese]

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DOI: 10.11805/tkyda2021356 Cite this Article

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 Copy Citation Text

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References

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[7] DUDCZYK J,KAWALEC A. Specific emitter identification based on graphical representation of the distribution of radar signal parameters[J]. Bulletin of the Polish Academy of Sciences:Technical Sciences, 2015,63(2):391-396. doi:10.1515/bpasts-2015-0044.

[8] KULIN M,KAZAZ T,MOERMAN I,et al. End-to-end learning from spectrum data:a deep learning approach for wireless signal identification in spectrum monitoring applications[J]. IEEE Access, 2017(6):18484-18501. doi:10.1109/ACCESS.2018.2818794.

[10] RIYAZ S, SANKHE K, IOANNIDIS S, et al. Deep learning Convolutional Neural Networks for radio identification[J]. IEEE Communications Magazine, 2018,56(9):146-152. doi:10.1109/MCOM.2018.1800153.

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