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 are turned off due to NES or incidents, e.g., disasters, hardware failures, or outages. 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, with a reduction of approximately 24\% compared to the conventional all GBS ON configuration, 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6G网络节能场景下无人机辅助的弹性视角。具体而言，我们考虑地面网络中存在多个地面基站，每个地面基站包含三个不同的扇区/小区，其中多个小区因网络节能或突发事件（如灾害、硬件故障或中断）而关闭。为解决此问题，我们提出了一种多智能体深度确定性策略梯度框架，通过在地面基站休眠策略下联合优化无人机轨迹、发射功率以及用户-无人机关联，以实现无人机辅助通信。该框架旨在保障网络中活跃用户的弹性以及无人机的长期可操作性。具体而言，它在最大化断电或网络节能区域用户服务覆盖的同时，最小化无人机的能量消耗。仿真结果表明，所提出的多智能体深度确定性策略梯度策略在不同测试周期中均能持续实现高覆盖率，优于其他基线方法。此外，该框架实现了最低的总能量消耗，相比传统的地面基站全开启配置降低了约24%，同时保持了相当的用户服务率。这些结果证实了所提方法在实现能效与服务性能间优越权衡的有效性，为可持续且具备弹性的无人机辅助蜂窝网络的发展提供了支持。