Deep Learning Based Radio Frequency Fingerprint Identification by Exploiting Spatial Stereoscopic Features
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63992-0_34
Reference12 articles.
1. Xie, N., Li, Z., Tan, H.: A survey of physical-layer authentication in wireless communications. IEEE Commun. Surv. Tutorials 23(1), 282–310 (2021)
2. Peng, L., Zhang, J., Liu, M., Hu, A.: DL based RFFI using differential constellation trace figure. IEEE Trans. Veh. Technol. 69(1), 1091–1095 (2020)
3. Yu, J., Hu, A., Li, G., Peng, L.: A robust RF fingerprinting approach using multisampling convolutional neural network. IEEE Internet Things J. 6(4), 6786–6799 (2019)
4. Pan, Y., Yang, S., Peng, H., et al.: Specific emitter identification based on deep residual networks. IEEE Access 7, 54425–54434 (2019)
5. Youssef, K., Bouchard, L., Haigh, K., et al.: Machine learning approach to RF transmitter identification. IEEE J. Radio Freq. Identif. 2(4), 197–205 (2018)
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