  Multi-access Edge Computing (MEC) addresses computational and battery
limitations in devices by allowing them to offload computation tasks. To
overcome the difficulties in establishing line-of-sight connections,
integrating unmanned aerial vehicles (UAVs) has proven beneficial, offering
enhanced data exchange, rapid deployment, and mobility. The utilization of
reconfigurable intelligent surfaces (RIS), specifically simultaneously
transmitting and reflecting RIS (STAR-RIS) technology, further extends coverage
capabilities and introduces flexibility in MEC. This study explores the
integration of UAV and STAR-RIS to facilitate communication between IoT devices
and an MEC server. The formulated problem aims to minimize energy consumption
for IoT devices and aerial STAR-RIS by jointly optimizing task offloading,
aerial STAR-RIS trajectory, amplitude and phase shift coefficients, and
transmit power. Given the non-convexity of the problem and the dynamic
environment, solving it directly within a polynomial time frame is challenging.
Therefore, deep reinforcement learning (DRL), particularly proximal policy
optimization (PPO), is introduced for its sample efficiency and stability.
Simulation results illustrate the effectiveness of the proposed system compared
to benchmark schemes in the literature.
