Е-Публикации
Технически университет - София

Abstract: Physical layer security is a promising technology for non-terrestrial wireless networks due to the lack of infrastructure over the low-altitude and high-altitude uncrewed aerial vehicle platforms. Physical layer security can be addressed using key-based and keyless approaches, such as channel adaptation and channel coding. In the key-based approach, the legitimate users generate secret keys by exploiting the unique random channel gains, which can be further used for information encryption and decryption. In non-terrestrial networks, the physical layer exploitation and the matching of generated secret keys on legitimate ends become more complex due to the 3-dimensional channel models, and the high mobility of uncrewed aerial vehicles, resulting in nonzero key. To address this challenge, this paper proposes an artificial intelligence-based key exchange mechanism where the secret keys are generated on the legitimate nodes of the communications links. The transmitter is responsible for training the neural network and transmitting the final synapses to the receiver to reconcile the information. The results are presented in terms of secret key waveforms. The success rate against the number of errors that occur in a single 128-bit key and 256-bit key shows a significant improvement.

References

1. Y. Hokazono, H. Kohara, Y. Kishiyama, and T. Asai, "Extreme coverage extension in 6G: Cooperative non-terrestrial network architecture integrating terrestrial networks, " in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), pp. 138-143, IEEE, 2022.
2. A. U. Haq, B. Muhammad, and A. Mihovska, "UAV communication aided by intelligent reflective surfaces-a performance analysis in the context of Industry 4. 0, " in the International Symposium on Wireless Personal Multimedia Communication (WPMC), 09 2022.
3. D. S. Karas, G. K. Karagiannidis, and R. Schober, "Neural network based phy-layer key exchange for wireless communications, " in Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1233-1238, IEEE, 2011.
4. Z. Hou and X. Zhang, "Secret key generation scheme based on generative adversarial networks in FDD systems, " in Proceeding of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1-6, 2021.
5. X. Wei and D. Saha, "KNEW: Key generation using neural networks from wireless channels, " in Proceedings of the ACM Workshop on Wireless Security and Machine Learning, pp. 45-50, 2022.
6. L. Jiao, G. Sun, J. Le, and K. Zeng, "Machine learning-assisted wireless PHY key generation with reconfigurable intelligent surfaces, " in Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning, pp. 61-66, 2021.
7. L. Zhao, X. Zhang, J. Chen, and L. Zhou, "Physical layer security in the age of artificial intelligence and edge computing, " IEEE Wireless Communications, vol. 27, no. 5, pp. 174-180, 2020.
8. G. Bagheri, A. K. Boroujeni, and S. Köpsell, "Machine learningbased vector quantization for secret key generation in physical layer security, " in Proceeding of the Global Information Infrastructure and Networking Symposium (GIIS), pp. 1-7, IEEE, 2024.
9. X. Zhang, G. Li, J. Zhang, A. Hu, Z. Hou, and B. Xiao, "Deeplearning-based physical-layer secret key generation for FDD systems, " IEEE Internet of Things Journal, vol. 9, no. 8, pp. 6081-6094, 2021.
10. S. Mathur, W. Trappe, N. Mandayam, C. Ye, and A. Reznik, "Radiotelepathy: Extracting a secret key from an unauthenticated wireless channel, " in Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, pp. 128-139, 2008.
11. M. Ramachandran, G. Surabhi, and A. Chockalingam, "OTFS: A new modulation scheme for high-mobility use cases, " Journal of the Indian Institute of Science, vol. 100, pp. 315-336, 2020.
12. R. Hadani, S. Rakib, M. Tsatsanis, A. Monk, A. J. Goldsmith, A. F. Molisch, and R. Calderbank, "Orthogonal time frequency space modulation, " in Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, IEEE, 2017.
13. M. Adil, S. Wyne, and S. J. Nawaz, "On quantization for secret key generation from wireless channel samples, " IEEE Access, vol. 9, pp. 21653-21668, 2021.
14. J. Zhang, B. He, T. Q. Duong, and R. Woods, "On the key generation from correlated wireless channels, " IEEE Communications Letters, vol. 21, no. 4, pp. 961-964, 2017.

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