Multi-access edge computing (MEC)-enabled integrated space-air-ground (SAG) networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). Leveraging the ample bandwidth in the terahertz (THz) spectrum, in this paper, we propose MEC-enabled integrated SAG networks with collaboration among unmanned aerial vehicles (UAVs). We then formulate the problem of minimizing the energy consumption of devices and UAVs in the proposed MEC-enabled integrated SAG networks by optimizing tasks offloading decisions, THz sub-bands assignment, transmit power control, and UAVs deployment. The formulated problem is a mixed-integer nonlinear programming (MILP) problem with a non-convex structure, which is challenging to solve. We thus propose a block coordinate descent (BCD) approach to decompose the problem into four sub-problems: 1) device task offloading decision problem, 2) THz sub-band assignment and power control problem, 3) UAV deployment problem, and 4) UAV task offloading decision problem. We then propose to use a matching game, concave-convex procedure (CCP) method, successive convex approximation (SCA), and block successive upper-bound minimization (BSUM) approaches for solving the individual subproblems. Finally, extensive simulations are performed to demonstrate the effectiveness of our proposed algorithm.

翻译：多接入边缘计算（MEC）赋能的空天地一体化网络近年来受到广泛关注，因其能够为缺乏地面基站的区域中的无线设备提供通信与计算服务。利用太赫兹频谱的充沛带宽，本文提出了一种通过无人机协作的MEC赋能空天地一体化网络。随后，我们通过优化任务卸载决策、太赫兹子带分配、发射功率控制及无人机部署，构建了最小化设备与无人机能耗的优化问题。该问题为具有非凸结构的混合整数非线性规划问题，求解难度较大。为此，我们提出基于块坐标下降的方法将原问题分解为四个子问题：1）设备任务卸载决策问题，2）太赫兹子带分配与功率控制问题，3）无人机部署问题，及4）无人机任务卸载决策问题。随后分别采用匹配博弈、凸凹过程方法、逐次凸逼近及块逐次上界最小化方法求解各子问题。最后，通过大量仿真验证了所提算法的有效性。