In this article, we study the computation offloading problem in hybrid edge-cloud based space-air-ground integrated networks (SAGIN), where joint optimization of partial computation offloading, unmanned aerial vehicle (UAV) trajectory control, user scheduling, edge-cloud computation, radio resource allocation, and admission control is performed. Specifically, the considered SAGIN employs multiple UAV-mounted edge servers with controllable UAV trajectory and a cloud sever which can be reached by ground users (GUs) via multi-hop low-earth-orbit (LEO) satellite communications. This design aims to minimize the weighted energy consumption of the GUs and UAVs while satisfying the maximum delay constraints of underlying computation tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we use the alternating optimization approach where we iteratively solve four sub-problems, namely user scheduling, partial offloading control and bit allocation over time slots, computation resource and bandwidth allocation, and multi-UAV trajectory control until convergence. Moreover, feasibility verification and admission control strategies are proposed to handle overloaded network scenarios. Furthermore, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex computation resource and bandwidth allocation and UAV trajectory control sub-problems. Via extensive numerical studies, we illustrate the effectiveness of our proposed design compared to baselines.

中文翻译：

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SAGIN 中的集成计算卸载、无人机轨迹控制、边缘云和无线电资源分配

在本文中，我们研究了基于混合边缘云的空地一体化网络（SAGIN）中的计算卸载问题，其中部分计算卸载、无人机（UAV）轨迹控制、用户调度、边缘云的联合优化执行计算、无线电资源分配和准入控制。具体来说，所考虑的 SAGIN 采用多个无人机安装的边缘服务器，这些服务器具有可控的无人机轨迹和一个云服务器，地面用户 (GU) 可以通过多跳低地球轨道 (LEO) 卫星通信到达云服务器。该设计旨在最小化GU和UAV的加权能耗，同时满足底层计算任务的最大延迟约束。为了解决底层的非凸混合整数非线性优化问题，我们使用交替优化方法，迭代解决四个子问题，即用户调度、部分卸载控制和时隙上的比特分配、计算资源和带宽分配，以及多无人机轨迹控制直至收敛。此外，还提出了可行性验证和准入控制策略来处理过载的网络场景。此外，采用逐次凸逼近（SCA）方法凸化并求解非凸计算资源和带宽分配以及无人机轨迹控制子问题。通过广泛的数值研究，我们说明了我们提出的设计与基线相比的有效性。