Secrecy Performance Optimization of Unmanned Aerial Vehicle -aided Physical Layer Security

GAO Yuan; TAN Rongjun; DENG Zhixiang

doi:10.11999/JEIT210600

Volume 44 Issue 8

Aug. 2022

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GAO Yuan, TAN Rongjun, DENG Zhixiang. Secrecy Performance Optimization of Unmanned Aerial Vehicle -aided Physical Layer Security[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2730-2738. doi: 10.11999/JEIT210600

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GAO Yuan, TAN Rongjun, DENG Zhixiang. Secrecy Performance Optimization of Unmanned Aerial Vehicle -aided Physical Layer Security[J]. Journal of Electronics & Information Technology, 2022, 44(8): 2730-2738. doi: 10.11999/JEIT210600

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Secrecy Performance Optimization of Unmanned Aerial Vehicle -aided Physical Layer Security

doi: 10.11999/JEIT210600

College of IoT Engineering, Hohai University, Nanjing 210098, China

Funds: The National Key Research and Development Project (2018YFC0407101), Jiangsu Overseas Visiting Scholar Program for University Prominent Young and Mid-aged Teachers and Presidents (2019)

Received Date: 2021-06-18
Accepted Date: 2021-11-18
Rev Recd Date: 2021-11-14

Available Online: 2021-11-25

Publish Date: 2022-08-17

Abstract

Abstract

Physical layer security is effective to solve the problem of communication security of Internet of Things (IoT). As a full duplex eavesdropper with active attack and passive eavesdropping exists in the IoT, Unmanned Aerial Vehicle (UAV) transmits artificial noise to the eavesdropper for improving secrecy performance in this paper. Based on estimating eavesdropper position, a trajectory optimization algorithm based on Q-learning is proposed to track the eavesdropper mobility and obtain the optimal secrecy performance. The results show that the proposed algorithm converges quickly. UAV can track the eavesdropper mobility to determine the best position of jamming the eavesdropping channel, which guarantees maximum achievable secrecy rate.
- Physical layer security,
- Artificial noise,
- Q-learning,
- Achievable secrecy rate,
- Unmanned Aerial Vehicle (UAV) trajectory optimization

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References(19)

References

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