UAV Onboard STAR-RIS Service Enhancement Mechanism Based on Deep Reinforcement Learning

UAVs and reconfigurable intelligent surfaces (RISs) have emerged as promising solutions to enhance communication coverage and performance. However, existing studies primarily focus on optimizing the amplitude and phase shift of a STAR-RIS without considering the impact of varying UAV hovering angles on signal reflection and transmission. In this paper, we propose a novel STAR-RIS-assisted UAV service enhancement mechanism that dynamically adjusts reflection/transmission regions based on the real-time user distribution, significantly improving the channel quality for both edge and occluded users. This work is the first to jointly optimize the phase and amplitude of the STAR-RIS, the UAV flight trajectory, and the hovering angle, addressing the critical challenge of co-channel interference caused by dynamically partitioned service areas. The complex optimization problem is decomposed into subproblems, where the UAV flight trajectory is optimized using the Chained Lin-Kernighan (CLK) algorithm and the STAR-RIS parameters and UAV hovering angle are optimized using the TD3 algorithm. The experimental results show that the proposed mechanism effectively reduces the system service time and user transmission time, outperforming traditional methods.