强化学习
计算机科学
隐蔽的
水准点(测量)
发射机
传输(电信)
实时计算
人工智能
计算机网络
电信
哲学
语言学
频道(广播)
大地测量学
地理
作者
Songjiao Bi,Langtao Hu,Quanjin Liu,Juan Wu,Rui Yang,Liaoni Wu
出处
期刊:China Communications
[Institute of Electrical and Electronics Engineers]
日期:2023-12-01
卷期号:20 (12): 131-141
被引量:2
标识
DOI:10.23919/jcc.ea.2022-0336.202302
摘要
Covert communications can hide the existence of a transmission from the transmitter to receiver. This paper considers an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) covert communication system. It was inspired by the high-dimensional data processing and decision-making capabilities of the deep reinforcement learning (DRL) algorithm. In order to improve the covert communication performance, an UAV 3D trajectory and IRS phase optimization algorithm based on double deep Q network (TAP-DDQN) is proposed. The simulations show that TAP-DDQN can significantly improve the covert performance of the IRS-assisted UAV covert communication system, compared with benchmark solutions.
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