期刊
IEEE WIRELESS COMMUNICATIONS LETTERS
卷 10, 期 8, 页码 1643-1647出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LWC.2021.3075996
关键词
Array signal processing; Unmanned aerial vehicles; Neural networks; Eavesdropping; Wireless communication; Training; Three-dimensional displays; Beamforming; unmanned aerial vehicle (UAV); physical layer security; deep learning (DL); secrecy rate maximization
资金
- National Natural Science Foundation of China [61671465, 61871396]
This study introduces a 3D beamforming method based on deep learning for physical layer security design in UAV communication systems. Simulation experiments demonstrate that this method can achieve better secrecy rate and flexible beam steering compared to benchmarks.
Beamforming is a promising technique to enhance the security of wireless transmission, while the optimal beamforming design with partial channel state informing (CSI) is challenging. This letter develops a three-dimensional (3D) robust beamforming method for unmanned aerial vehicle (UAV) communication systems in the physical layer security perspective. Specifically, aiming at maximizing the average secrecy rate of the considered system, a precisely designed neural network is trained to optimize the beamformer for confidential signal and artificial noise (AN), with partial CSIs of legitimate UAV and eavesdropping UAV. Simulation experiments show that the proposed deep learning (DL) based method could achieve better secrecy rate and flexible beam steering than benchmarks.
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