Ensuring network resilience in 6G and beyond is essential to maintain service continuity during base station (BS) outages due to failures, disasters, attacks, or energy-saving operations. This paper proposes a novel resilience optimization framework for integrated satellite-terrestrial networks (ISTNs), leveraging low Earth orbit (LEO) satellites to assist users when terrestrial BSs are unavailable. Specifically, we develop a realistic multi-cell model incorporating user association, antenna downtilt adaptation, power control, heterogeneous traffic demands, and dynamic user distribution. The objective is to maximize of the total user rate in the considered area by optimizing the BS's antenna tilt, transmission power, user association to neighboring BS or to a LEO satellite with a minimum number of successfully served user satisfaction constraint, defined by rate and Reference Signal Received Power (RSRP) requirements. To solve the non-convex, NP-hard problem, we design a deep Q-network (DQN)-based algorithm to learn network dynamics to maximize throughput while minimizing LEO satellite usage, thereby limiting reliance on links with longer propagation delays and prolonging satellite operational lifetime. Simulation results confirm that our approach significantly outperforms the benchmark one.

翻译：在6G及未来网络中，确保网络韧性对于在基站因故障、灾害、攻击或节能操作而中断时维持服务连续性至关重要。本文提出了一种新颖的星地融合网络韧性优化框架，利用低地球轨道卫星在地面基站不可用时为用户提供协助。具体而言，我们建立了一个真实的多小区模型，该模型综合考虑了用户关联、天线下倾角调整、功率控制、异构业务需求以及动态用户分布。目标是通过优化基站天线倾角、发射功率、用户与相邻基站或LEO卫星的关联，在满足由速率和参考信号接收功率要求定义的最小成功服务用户满意度约束下，最大化考虑区域内的总用户速率。为解决这一非凸、NP难问题，我们设计了一种基于深度Q网络的算法，以学习网络动态，在最大化吞吐量的同时最小化LEO卫星使用量，从而限制对具有较长传播时延链路的依赖并延长卫星运行寿命。仿真结果证实，我们的方法显著优于基准方案。