Abstract

Deploying low-altitude unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is a promising approach to enhance cellular network coverage in dynamic and infrastructure-constrained environments. However, real-world UAV deployment involves complex challenges, including managing partially observable and mobile users, maintaining multihop backhaul connectivity to a fixed ground base station, mitigating co-channel interference via minimum UAV separation, and optimizing coverage efficiency, fairness, and energy consumption. This article proposes a multiagent deep reinforcement learning (MARL) framework that formulates the dynamic UAV deployment task as a centralized multiple-input-multiple-output policy. Our method employs sequential action masking to guarantee multihop connectivity and enforce minimum distance constraints during UAV placement. The policy network integrates graph neural networks, residual convolutional layers, and feature-wise linear modulation to effectively capture spatial relations, inter-UAV interactions, and global and local network states. We validate our approach through extensive simulations across various user density scenarios, demonstrating significant improvements in coverage efficiency, fairness of coverage distribution, and flight-energy reduction compared to strong baseline algorithms. Our proposed framework provides a comprehensive solution that simultaneously addresses dynamic user mobility, multihop connectivity, interference mitigation, and energy efficiency in UAV-assisted cellular networks.