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

TL;DR

This paper introduces EMTAD, a scalable algorithm for real-time multi-UAV deployment that optimizes positions based on traffic demands, improving network performance and reducing UAV deployment costs.

Contribution

The paper presents a novel joint optimization framework for UAV placement and deployment minimization tailored for dynamic traffic-aware wireless networks.

Findings

EMTAD outperforms existing methods in network throughput.

It reduces UAV deployment overhead significantly.

The approach adapts effectively to real-time traffic changes.

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler for next-generation wireless networks due to their on-demand deployment, high mobility, and ability to provide Line-of-Sight (LoS) connectivity. These features make UAVs particularly well-suited for dynamic and mission-critical applications such as intelligent transportation systems and emergency communications. However, effectively positioning multiple UAVs in real-time to meet non-uniform, time-varying traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose an Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) Algorithm, a scalable and adaptive framework that dynamically adjusts UAV placements based on real-time user locations and spatial traffic distribution. In contrast to existing methods, EMTAD jointly optimizes UAV positioning and minimizes the number of deployed UAVs, ensuring efficient UE-UAV association while satisfying the traffic demand of users. Simulation results demonstrate that EMTAD significantly improves network performance while reducing deployment overhead by minimizing the number of UAVs required in dynamic and traffic-aware environments.