Channel-Robust Specific Emitter Identification Based on Transformer
DOI:
https://doi.org/10.54097/hset.v7i.1019Keywords:
Specific Emitter Identification, Transformer, Robust ClassificationAbstract
Specific emitter identification (SEI) refers to the process of identifying emitter individuals based on corresponding wireless signals. Although deep learning has been successfully applied in SEI, the performance remains to be improved when the channel changes. In this paper, we suggest that a potential reason of the performance degradation is inadequacy of model capacity. Therefore, Transformer, an advanced neural network architecture with large model capacity, is applied for channel-robust SEI. Experimental results show that Transformer achieves better performance than conventional convolutional neural networks.
Downloads
References
K. Talbot, P. Duley, and M. Hyatt, “Specific emitter identification and verification,” Technology Review, vol. 113, 2003.
G. Baldini, G. Steri, and R. Giuliani, “Identification of wireless devices from their physical layer radio-frequency fingerprints,” in Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global, 2018, pp. 6136–6146.
E. Spezio Anthony, “Electronic warfare systems,” IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 3, pp. 633–644, 2002.
O. Ureten and N. Serinken, “Wireless security through rf fingerprinting,” Canadian Journal of Electrical and Computer Engineering, vol. 32, no. 1, pp. 27–33, 2007.
B. He and F. Wang, “Cooperative specific emitter identification via multiple distorted receivers,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3791–3806, 2020.
Q. Wu, C. Feres, D. Kuzmenko, D. Zhi, Z. Yu, X. Liu et al., “Deep learning based rf fingerprinting for device identification and wireless security,” Electronics Letters, vol. 54, no. 24, pp. 1405–1407, 2018.
R. Shamnaz, S. Kunal, I. Stratis, and C. Kaushik, “Deep learning convolutional neural networks for radio identification,” IEEE Communications Magazine, vol. 56, no. 9, pp. 146–152, 2018.
L. Ding, S. Wang, F. Wang, and W. Zhang, “Specific emitter identification via convolutional neural networks,” IEEE Communications Letters, vol. 22, no. 12, pp. 2591–2594, 2018.
Y. Pan, S. Yang, H. Peng, T. Li, and W. Wang, “Specific emitter identification based on deep residual networks,” IEEE Access, vol. 7, pp. 54 425–54 434, 2019.
L. Yun, T. Ya, D. Zheng, C. Lei, and M. Shiwen, “Contour stella image and deep learning for signal recognition in the physical layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 1, pp. 34–46, 2020.
W. Yu, G. Guan, G. Haris, O. Tomoaki, D. Octavia A, and P. H Vincent, “An efficient specific emitter identification method based on complexvalued neural networks and network compression,” IEEE Journal on Selected Areas in Communications, 2021.
S. Nasim, S. Kunal, D. Jennifer, I. Stratis, and C. Kaushik, “More is better: Data augmentation for channel-resilient rf fingerprinting,” IEEE Communications Magazine, vol. 58, no. 10, pp. 66–72, 2020.
A. Vaswani, N. Shazeer, N. Parmar, et al. “Attention is all you need”, in Advances in neural information processing systems, 2017.
G. Zerveas, S. Jayaraman, D. Patel, et al. “A transformer-based framework for multivariate time series representation learning”, in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2021: 2114-2124.
M. V. d. Laurens and H. Geoffrey, “Visualizing data using t-sne.” Journal of machine learning research, vol. 9, no. 11, 2008.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
