Traffic-Aware UAV Placement Using a Generalizable Deep Reinforcement Learning Methodology

TL;DR

This paper introduces TUPA, a deep reinforcement learning-based algorithm that optimizes UAV placement for dynamic traffic demands, significantly improving network utility without retraining in new scenarios.

Contribution

The paper presents a novel DRL-based method for traffic-aware UAV placement that generalizes to unseen scenarios, enhancing network utility in dynamic environments.

Findings

TUPA increases network utility up to 4x compared to baseline solutions.

The DRL approach enables autonomous adaptation to changing traffic demands.

TUPA generalizes knowledge to unknown user configurations without additional training.

Abstract

Unmanned Aerial Vehicles (UAVs) acting as Flying Access Points (FAPs) are being used to provide on-demand wireless connectivity in extreme scenarios. Despite ongoing research, the optimization of UAVs' positions according to dynamic users' traffic demands remains challenging. We propose the Traffic-aware UAV Placement Algorithm (TUPA), which positions a UAV acting as FAP according to the users' traffic demands, in order to maximize the network utility. Using a DRL approach enables the FAP to autonomously learn and adapt to dynamic conditions and requirements of networking scenarios. Moreover, the proposed DRL methodology allows TUPA to generalize knowledge acquired during training to unknown combinations of users' positions and traffic demands, with no additional training. TUPA is trained and evaluated using network simulator ns-3 and ns3-gym framework. The results demonstrate that TUPA increases the network utility, compared to baseline solutions, increasing the average network utility up to 4x in scenarios with heterogeneous traffic demands.