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.

翻译：多接入边缘计算（MEC）通过允许设备卸载计算任务，解决了设备计算能力与电池容量的局限性。为克服视距链路建立的困难，无人机（UAV）的集成被证明具有显著优势，能够增强数据交换、实现快速部署并提升移动性。可重构智能表面（RIS）技术，特别是同时发射与反射RIS（STAR-RIS）技术，进一步扩展了覆盖能力并增强了MEC的灵活性。本研究探索了UAV与STAR-RIS的集成方案，以促进物联网设备与MEC服务器之间的通信。所构建的问题旨在通过联合优化任务卸载决策、空中STAR-RIS轨迹、幅度与相位偏移系数以及发射功率，最小化物联网设备与空中STAR-RIS的总能耗。鉴于问题的非凸性及动态环境特性，在多项式时间内直接求解具有挑战性。因此，引入深度强化学习（DRL），特别是近端策略优化（PPO）算法，以利用其样本效率与稳定性优势。仿真结果表明，与文献中的基准方案相比，所提系统具有有效性。