This paper investigates the unmanned aerial vehicle (UAV)-assisted resilience perspective in the 6G network energy saving (NES) scenario. More specifically, we consider multiple ground base stations (GBSs) and each GBS has three different sectors/cells in the terrestrial networks, and multiple cells may become inactive due to unexpected events such as power outages, disasters, hardware failures, or erroneous energy-saving decisions made by external network management systems. During the time required to reactivate these cells, UAVs are deployed to temporarily restore user service. To address this, we propose a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to enable UAV-assisted communication by jointly optimizing UAV trajectories, transmission power, and user-UAV association under a sleeping ground base station (GBS) strategy. This framework aims to ensure the resilience of active users in the network and the long-term operability of UAVs. Specifically, it maximizes service coverage for users during power outages or NES zones, while minimizing the energy consumption of UAVs. Simulation results demonstrate that the proposed MADDPG policy consistently achieves high coverage ratio across different testing episodes, outperforming other baselines. Moreover, the MADDPG framework attains the lowest total energy consumption, while maintaining a comparable user service rate. These results confirm the effectiveness of the proposed approach in achieving a superior trade-off between energy efficiency and service performance, supporting the development of sustainable and resilient UAV-assisted cellular networks.

翻译：本文研究了无人机辅助的韧性视角在6G网络节能场景中的应用。具体而言，我们考虑多个地面基站，每个基站在地面网络中有三个不同的扇区/小区，而部分小区可能因停电、灾难、硬件故障或外部网络管理系统做出的错误节能决策等意外事件而失效。在重新激活这些小区所需的时间内，部署无人机以临时恢复用户服务。为解决这一问题，我们提出了一种多智能体深度确定性策略梯度框架，通过联合优化无人机轨迹、发射功率以及用户-无人机关联，在休眠地面基站策略下实现无人机辅助通信。该框架旨在保障网络中活跃用户的韧性以及无人机的长期可操作性。具体而言，它在停电或节能区域中最大化用户的服务覆盖范围，同时最小化无人机的能耗。仿真结果表明，所提出的MADDPG策略在不同测试回合中持续实现了高覆盖率，优于其他基准方法。此外，该框架在保持可比较的用户服务率的同时，实现了最低的总能耗。这些结果证实了所提方法在实现能效与服务性能之间优越平衡方面的有效性，支持了可持续且具有韧性的无人机辅助蜂窝网络的发展。