In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders' decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.

翻译：在物联网时代，多接入边缘计算使能的星-空-地一体化网络已成为一项前景广阔的技术，可为海量物联网设备提供无缝、可靠的通信与计算服务。本文研究低轨卫星、高空平台与地面基站的协同合作，为广泛分布的物联网设备提供中继与计算服务。考虑到动态星-空-地一体化系统中的不确定性，我们构建了一个随机优化问题，通过联合优化资源分配与任务卸载，在满足能量约束的同时最小化时间平均期望服务时延。为解决该问题，我们首先设计了一种基于李雅普诺夫方法的在线控制算法，将原问题分解为多个单时隙子问题。由于每个单时隙子问题均属于大规模混合整数非线性规划问题，经典计算机难以求解，我们进一步提出新型混合量子-经典广义Benders分解算法，通过利用量子并行计算优势实现高效求解。数值结果验证了所提出的MEC使能星-空-地一体化网络方案的有效性。