Deep reinforcement learning-based sum rate maximization for RIS-assisted ISAC-UAV network

Recent advances in communications technologies have paved the way for integrating communication and sensing functionalities into unmanned aerial vehicle (UAV) networks by using reconfigurable intelligent surfaces (RIS). In this paper, we propose a novel approach to maximize the sum rate of RIS-assisted UAV networks by using an integrated sensing and communications (ISAC) network in conjunction with deep reinforcement learning (DRL). The integration of UAVs with ISAC networks results in dynamic and unpredictable channel conditions, which reduces the effectiveness of traditional optimization techniques. To address this challenge, we develop a DRL-based sum-rate maximization algorithm that adaptively configures the beamforming matrix and RIS phase shifts to optimize the communication performance while achieving the signal-to-noise ratio required for sensing. Our simulation results indicate that the proposed algorithm significantly outperforms the existing methods in terms of sum rate while accommodating the dynamic nature of the ISAC-UAV network.