UAV-Enabled Aerial Monitoring Aided by STAR-RIS: A Stochastic Optimization Framework

This paper studies the unmanned aerial vehicle (UAV)-enabled aerial monitoring assisted by simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), in which one UAV aims to monitor a number of moving targets, and one STAR-RIS is installed on a building for assisting the UAV to broadcast the monitored information to both indoor and outdoor users. Due to the randomness of target movements over time, the UAV needs to adaptively adjust its flight trajectory to track them. This thus results in highly dynamic channel conditions and uncertain UAV energy consumption, which accordingly make the efficient aerial monitoring a challenging task. To address these challenges, we propose a STAR-RIS-aided UAV-enabled aerial monitoring framework, which aims to maximize the long-term average throughput for all users, through joint optimization of transmit beamforming, UAV trajectory, and STAR-RIS configuration, while ensuring the monitoring requirements under strict energy constraints. The formulated problem is a multi-stage stochastic optimization problem, due to the randomness of various system parameters. To handle this problem, we apply the Lyapunov optimization technique and introduce a virtual energy queue to transform it into a series of single-slot optimization subproblems that are solvable online. For each subproblem, we develop efficient algorithms to obtain a near-optimal solution, in which a penalty dual decomposition (PDD) approach is used for the transmit beamforming and STAR-RIS configuration optimization, and a sequential parametric convex approximation (SPCA) method is used for UAV trajectory optimization. Extensive simulations demonstrate that the proposed framework significantly outperforms benchmark schemes, effectively maximizing the throughput and energy efficiency under dynamic operational conditions.