In critical situations such as natural disasters, network outages, battlefield communication, or large-scale public events, Unmanned Aerial Vehicles (UAVs) offer a promising approach to maximize wireless coverage for affected users in the shortest possible time. In this paper, we propose a novel framework where multiple UAVs are deployed with the objective to maximize the number of served user equipment (UEs) while ensuring a predefined data rate threshold. UEs are initially clustered using a K-means algorithm, and UAVs are optimally positioned based on the UEs' spatial distribution. To optimize power allocation and mitigate inter-cluster interference, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, considering both LOS and NLOS fading. Simulation results demonstrate that our method significantly enhances UEs coverage and outperforms Deep Q-Network (DQN) and equal power distribution methods, improving their UE coverage by up to 2.07 times and 8.84 times, respectively.

翻译：在自然灾害、网络中断、战场通信或大规模公共事件等紧急情况下，无人机为在最短时间内最大化受影响用户的无线覆盖提供了一种极具前景的解决方案。本文提出了一种新颖的框架，其中部署多架无人机，旨在最大化所服务的用户设备数量，同时确保满足预定义的数据速率阈值。首先使用K-means算法对用户设备进行聚类，并根据用户设备的空间分布对无人机进行最优部署。为了优化功率分配并减轻簇间干扰，我们采用了多智能体深度确定性策略梯度算法，同时考虑了视距和非视距衰落。仿真结果表明，我们的方法显著提升了用户设备覆盖性能，优于深度Q网络和等功率分配方法，将它们的用户设备覆盖率分别提升了最高达2.07倍和8.84倍。