Online Resource Allocation and Trajectory Optimization of STAR–RIS–Assisted UAV–MEC System

In urban environments, the highly complex communication environment often leads to blockages in the link between ground users (GUs) and unmanned aerial vehicles (UAVs), resulting in poor communication quality. Although traditional reconfigurable intelligent surfaces (RISs) can improve wireless channel quality, they can only provide reflection services and have limited coverage. For this reason, we study a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR–RIS)–assisted UAV–mobile edge computing (UAV–MEC) network, which can serve multiple users residing in the transmission area and reflection area, and switch between reflection and transmission modes according to the relative positions of the UAV, GUs, and STAR–RIS, providing users with more flexible and efficient services. The system comprehensively considers user transmit power, time slot allocation, UAV flight trajectory, STAR–RIS mode selection, and phase angle matrix, achieving long–term energy consumpution minimization while ensuring stable task backlog queue. Since the proposed problem is a long–term stochastic optimization problem, we use the Lyapunov method to transform it into three deterministic online optimization subproblems and iteratively solve them alternately. Specifically, we firstly use the Lambert function to solve for the closed-form solution of the transmit power; then, use Lagrange duality and the Karush–Kuhn–Tucker conditions to solve time slot allocation; finally, successive convex approximation is used to obtain trajectory planning for UAVs with lower complexity, and triangular inequalities are used to solve the STAR–RIS phase shift. The simulation results show that the proposed scheme has better performance than other benchmark schemes in maintaining queue stability and reducing energy consumption.