Deep Reinforcement Learning for UAV Assisted AoI-Aware STAR-RIS Communication

The rapid development of the internet of things (IoT) has heightened the demand for timely data delivery in heterogeneous networks, making the age of information (AoI) a key performance metric. In this work, we consider an unmanned aerial vehicle (UAV)-assisted IoT network enhanced by a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) to improve data freshness. We formulate a joint optimization problem with respect to UAV trajectory, STARRIS phase shifts, and device scheduling to minimize the AoI. To tackle the problem's complexity, we employ a proximal policy optimization (PPO)-based reinforcement learning approach that autonomously discovers effective control policies. Simulation results demonstrate that our proposed solution reduces AoI, demonstrating enhanced adaptability, robustness, and communication performance in dynamic IoT environments.