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（MEC）使能的星-空-地一体化网络（SATIN）已成为一种有前景的技术，能够为海量物联网设备提供无缝且可靠的通信与计算服务。本文研究了低地球轨道（LEO）卫星、高空平台（HAP）和地面基站（BS）之间的协作，为广泛分布的物联网设备提供中继和计算服务。考虑到动态SATIN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和任务卸载，在满足能量约束的同时最小化时间平均期望服务延迟。为解决所构建的问题，我们首先开发了一种基于Lyapunov的在线控制算法，将其分解为多个单时隙子问题。由于每个单时隙子问题是一个大规模混合整数非线性规划（MINLP），对经典计算机而言难以处理，我们进一步提出了新颖的混合量子-经典广义Benders分解（HQCGBD）算法，利用量子计算在并行计算中的优势高效求解该问题。数值结果验证了所提出的MEC使能SATIN方案的有效性。