| Title |
Secrecy Rate Maximization for RIS-assisted UAV-ISAC Systems |
| Authors |
문상미(Sangmi Moon) ; 이창건(Chang-Gun Lee) ; 황인태(Intae Hwang) |
| DOI |
https://doi.org/10.5573/ieie.2024.61.11.3 |
| Keywords |
DDPG; DRL; ISAC; RIS; UAV |
| Abstract |
In this paper, we design a reconfigurable intelligent surfaces(RIS)-assisted unmanned aerial vehicle(UAV)-integrated sensing and communications(ISAC) system that simultaneously provides communication services to users while sensing an eavesdropper as a radar target. We propose a deep reinforcement learning-based beamforming algorithm to maximize the secrecy rate while meeting radar detection requirements in the RIS-assisted UAV-ISAC system. The proposed algorithm jointly designs the transmit beamforming of the base station and the passive beamforming of the RIS using the deep deterministic policy gradient (DDPG) method. Simulation results based on 3D ray tracing demonstrate that utilizing RIS and the proposed DDPG-based beamforming algorithm in the UAV-ISAC system significantly enhances the secrecy rate. |