Satellite networks with wide coverage are considered natural extensions to terrestrial networks for their long-distance end-to-end (E2E) service provisioning. However, the inherent topology dynamics of low earth orbit satellite networks and the uncertain network scales bring an inevitable requirement that resource chains for E2E service provisioning must be efficiently re-planned. Therefore, achieving highly adaptive resource management is of great significance in practical deployment applications. This paper first designs a regional resource management (RRM) mode and further formulates the RRM problem that can provide a unified decision space independent of the network scale. Subsequently, leveraging the RRM mode and deep reinforcement learning framework, we develop a topology feature-based dynamic and adaptive resource management algorithm to combat the varying network scales. The proposed algorithm successfully takes into account the fixed output dimension of the neural network and the changing resource chains for E2E service provisioning. The matched design of the service orientation information and phased reward function effectively improves the service performance of the algorithm under the RRM mode. The numerical results demonstrate that the proposed algorithm with the best convergence performance and fastest convergence rate significantly improves service performance for varying network scales, with gains over compared algorithms of more than 2.7%, 11.9%, and 10.2%, respectively.

翻译：具有广域覆盖特性的卫星网络因其能够提供长距离端到端服务，被视为地面网络的天然延伸。然而，低轨卫星网络固有的拓扑动态性与不确定的网络规模，使得端到端服务供给所需的资源链必须进行高效重规划，这一需求不可避免。因此，在实际部署应用中实现高度自适应的资源管理具有重要意义。本文首先设计了一种区域资源管理模式，并进一步构建了与该模式对应的资源管理问题，该问题能够提供一个与网络规模无关的统一决策空间。随后，结合区域资源管理模式与深度强化学习框架，我们提出了一种基于拓扑特征的动态自适应资源管理算法，以应对变化的网络规模。该算法成功兼顾了神经网络固定输出维度与端到端服务供给中变化资源链之间的矛盾。服务导向信息与阶段性奖励函数的匹配设计，有效提升了算法在区域资源管理模式下的服务性能。数值结果表明，所提算法具有最佳的收敛性能与最快的收敛速度，在不同网络规模下均显著改善了服务性能，相较于对比算法分别取得了超过2.7%、11.9%和10.2%的性能增益。