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Physical layer authentication in UAV-enabled relay networks based on manifold learning

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Abstract

An unmanned aerial vehicle (UAV) relay network is a promising solution in the next-generation wireless networks due to its high capacity and unlimited geography. However, because of the openness of wireless channels and UAV mobility, it is remarkably challenging to guarantee the secure access of UAV relay. In this paper, we investigate the physical layer authentication (PLA) to verify the identity of the UAV relay for preventing unauthorized access to users’ information or network service. Unlike most existing PLA methods for UAV, the proposed PLA scheme fully considers the time-varying of physical layer attributes caused by UAV mobility, and transforms the authentication problem into recognizing nonlinearly separable physical layer data. Particularly, we propose a manifold learning-based PLA scheme that can authenticate the mobile UAV relay in real time by establishing the local correlation of physical layer attributes. The Markov chain of physical layer data in the time domain is established to evaluate UAV state transition probability through the proposed diffusion map algorithm. The legitimate UAV and spoofing attackers can always be authenticated by the different motion states. Performance analysis offered a comprehensive understanding of the proposed scheme. Extensive simulations confirm that the performance of the proposed scheme improves over 18% in resisting the intelligent spoofing UAV compared with the traditional methods.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61932005, 61941105), Shenzhen Science and Technology Innovation Commission Free Exploring Basic Research Project (Grant No. 2021Szvup008), and 111 Project of China (Grant No. B16006).

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Authors and Affiliations

  1. National Engineering Lab for Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shida Xia, Xiaofeng Tao, Na Li, Shiji Wang & Jin Xu

  2. Beijing University of Posts and Telecommunications Research Institute, Shenzhen, 518000, China

    Na Li

Authors
  1. Shida Xia
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  2. Xiaofeng Tao
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  3. Na Li
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  4. Shiji Wang
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  5. Jin Xu
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Corresponding author

Correspondence to Xiaofeng Tao.

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Xia, S., Tao, X., Li, N. et al. Physical layer authentication in UAV-enabled relay networks based on manifold learning. Sci. China Inf. Sci. 65, 222302 (2022). https://doi.org/10.1007/s11432-021-3410-2

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  • DOI: https://doi.org/10.1007/s11432-021-3410-2

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