Traffic and Obstacle-aware UAV Positioning in Urban Environments Using Reinforcement Learning

TL;DR

This paper introduces RLTOPA, a reinforcement learning-based algorithm for UAV positioning that considers obstacles and traffic demands to optimize communication QoS in urban environments.

Contribution

It presents a novel traffic- and obstacle-aware UAV positioning algorithm using reinforcement learning to enhance network performance in complex environments.

Findings

Up to 95% improvement in aggregate throughput.

Up to 71% reduction in delay.

Maintains fairness while optimizing QoS.

Abstract

Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). In such environments, maintaining Line-of-Sight (LoS), especially at higher frequencies, is crucial for ensuring reliable communication networks with high capacity, particularly in environments with obstacles. The main contribution of this paper is a traffic- and obstacle-aware UAV positioning algorithm named Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm (RLTOPA), for such environments. RLTOPA determines the optimal position of the UAV by considering the positions of ground users, the coordinates of obstacles, and the traffic demands of users. This positioning aims to maximize QoS in terms of throughput by ensuring optimal LoS between ground users and the UAV. The network performance of the proposed solution, characterized in terms of mean delay and throughput, was evaluated using the ns- 3 simulator. The results show up to 95% improvement in aggregate throughput and 71% in delay without compromising fairness.