Deep reinforcement learning for IRS-assisted UAV covert communications
Songjiao Bi, Langtao Hu, Quanjin Liu, Jianlan Wu, Rui Yang, Lei Wu
Abstract
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.