To meet the demands for ubiquitous communication and temporary edge computing in 6G networks, aerial mobile edge computing (MEC) networks have been envisioned as a new paradigm. However, dynamic user requests pose challenges for task assignment strategies. Most of the existing research assumes that the strategy is deployed on ground-based stations or UAVs, which will be ineffective in an environment lacking infrastructure and continuous energy supply. Moreover, the resource mutual exclusion problem of dynamic task assignment has not been effectively solved. Toward this end, we introduce the digital twin (DT) into the aerial MEC network to study the resource coalition cooperation approach with the generative model (GM), which provides a preliminary coalition structure for the coalition game. Specifically, we propose a novel network framework that is composed of an application plane, a physical plane, and a virtual plane. After that, the task assignment problem is simplified to convex optimization programming with linear constraints. And then, we also propose a resource coalition cooperation approach that is based on a transferable utility (TU) coalition game to obtain an approximate optimal solution. Numerical results confirm the effectiveness of our proposed approach in terms of energy consumption and utilization of resources.

翻译：为满足6G网络中无处不在的通信需求和临时边缘计算需求，空中移动边缘计算网络已被视为一种新范式。然而，动态用户请求给任务分配策略带来了挑战。现有研究大多假设策略部署在地面基站或无人机上，这在缺乏基础设施和持续能源供给的环境中会失效。此外，动态任务分配中的资源互斥问题尚未得到有效解决。为此，我们将数字孪生引入空中MEC网络，研究基于生成模型的资源联盟协作方法，为联盟博弈提供初始联盟结构。具体而言，我们提出了一个由应用平面、物理平面和虚拟平面组成的新型网络框架。随后，将任务分配问题简化为具有线性约束的凸优化规划问题。进而，我们提出了一种基于可转移效用联盟博弈的资源联盟协作方法，以获得近似最优解。数值结果证实了所提方法在能耗和资源利用率方面的有效性。