Satellite-terrestrial integrated networks (STIN) are envisioned as a promising architecture for ubiquitous network connections to support diversified services. In this paper, we propose a novel resource slicing scheme with cross-cell coordination in STIN to satisfy distinct service delay requirements and efficient resource usage. To address the challenges posed by spatiotemporal dynamics in service demands and satellite mobility, we formulate the resource slicing problem into a long-term optimization problem and propose a distributed resource slicing (DRS) scheme for scalable and flexible resource management across different cells. Specifically, a hybrid data-model co-driven approach is developed, including an asynchronous multi-agent reinforcement learning-based algorithm to determine the optimal satellite set serving each cell and a distributed optimization-based algorithm to make the resource reservation decisions for each slice. Simulation results demonstrate that the proposed scheme outperforms benchmark methods in terms of resource usage and delay performance.

翻译：卫星-地面一体化网络（STIN）被视作实现泛在连接以支持多样化服务的理想架构。本文提出一种面向STIN的跨小区协同资源切片方案，以满足差异化服务时延需求并提升资源利用效率。针对服务需求的时空动态性与卫星移动性带来的挑战，我们将资源切片问题建模为长期优化问题，并提出分布式资源切片（DRS）方案以实现跨小区的可扩展灵活资源管理。具体而言，我们开发了一种数据-模型混合驱动方法：基于异步多智能体强化学习算法确定服务各小区的最优卫星集合，并采用分布式优化算法为每个切片制定资源预留决策。仿真结果表明，所提方案在资源利用率和时延性能方面均优于基准方法。