Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks

Unmanned Aerial Vehicles (UAVs) are a promising solution for next-generation wireless networks due to their mobility, rapid deployment, and ability to provide Line-of-Sight (LoS) connectivity. However, deploying multiple UAVs in realtime to meet dynamic, non-uniform traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose the Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) algorithm, a scalable algorithm that jointly minimizes UAV count and optimizes 3D positioning based on real-time user distribution and traffic demand. In contrast to prior works that assume static user patterns or fixed UAV counts, EMTAD dynamically adapts UAV deployment to maximize spectral efficiency and satisfy userspecific Quality of Service (QoS) requirements. Simulation results demonstrate that EMTAD reduces the number of UAVs required and achieves superior aggregate throughput compared to baseline approaches.