A Framework to Develop and Validate RL-Based Obstacle-Aware UAV Positioning Algorithms

TL;DR

This paper presents RLpos-3, a comprehensive framework combining reinforcement learning and simulation tools to develop and evaluate UAV positioning algorithms in obstacle-rich environments, improving wireless network QoS.

Contribution

Introduction of RLpos-3, a novel framework integrating RL and ns-3 for developing and benchmarking UAV positioning algorithms in complex environments.

Findings

RLpos-3 effectively optimizes UAV placement in urban environments.

Framework supports diverse environmental conditions and traffic demands.

Demonstrated improvements in network QoS through simulation use cases.

Abstract

Unmanned Aerial Vehicles (UAVs) increasingly enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic, obstacle-prone environments remains a significant research challenge due to their complexity. Reinforcement Learning (RL) offers adaptability and robustness in such environments, proving effective for UAV optimization. This paper introduces RLpos-3, a novel framework that integrates standard RL techniques and simulation libraries with Network Simulator 3 (ns-3) to facilitate the development and evaluation of UAV positioning algorithms. RLpos-3 serves as a supplementary tool for researchers, enabling the implementation, analysis, and benchmarking of UAV positioning strategies across diverse environmental conditions while meeting user traffic demands. To validate its effectiveness, we present use cases demonstrating RLpos-3's performance in optimizing UAV placement under realistic conditions, such as urban and obstacle-rich environments.