A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhanced unnamed aerial vehicle (UAV)-enabled multi-user mobile edge computing (MEC) scheme is proposed in this paper. Different from the existing MEC works, the proposed scheme allows bi-directional offloading where users can simultaneously offload their computing tasks to the MEC servers situated at the ground base station (BS) and aerial UAV with the assistance of the STARRIS. Specifically, we formulate an optimization problem aiming at maximizing the energy efficiency of the system while ensuring the quality of service (QoS) constraint by jointly optimizing the resource allocation, user scheduling, passive beamforming of the STAR-RIS, and the UAV trajectory. An iterative algorithm designed with the Dinkelbach's algorithm and the successive convex approximation (SCA) is proposed to effectively handle the formulated non-convex optimization problem. Simulation results indicate that the proposed STAR-RIS enhanced UAV-enabled MEC scheme possesses significant advantages in enhancing the system energy efficiency over other baseline schemes including the conventional RIS-aided scheme.

翻译：本文提出一种同时透射与反射可重构智能表面（STAR-RIS）增强的无人机（UAV）辅助多用户移动边缘计算（MEC）方案。与现有MEC研究不同，本文方案允许双向卸载：用户可在STAR-RIS辅助下，同时将计算任务卸载至地面基站（BS）和空中无人机上的MEC服务器。具体而言，我们构建了一个优化问题，通过联合优化资源分配、用户调度、STAR-RIS无源波束赋形及无人机轨迹，在确保服务质量（QoS）约束的同时最大化系统能量效率。为有效处理所提出的非凸优化问题，本文设计了基于Dinkelbach算法与逐次凸近似（SCA）的迭代算法。仿真结果表明，与包括传统RIS辅助方案在内的基线方案相比，本文提出的STAR-RIS增强无人机MEC方案在提升系统能量效率方面具有显著优势。