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.
@article{arxiv.2503.23669,
title = {Multi-Agent Deep Reinforcement Learning for Optimized Multi-UAV Coverage and Power-Efficient UE Connectivity},
author = {Xuli Cai and Poonam Lohan and Burak Kantarci},
journal= {arXiv preprint arXiv:2503.23669},
year = {2025}
}