Rate Adaptation Aware Positioning for Flying Gateways using Reinforcement Learning

TL;DR

This paper introduces RARL, a reinforcement learning-based algorithm for UAV positioning that considers rate adaptation effects to optimize wireless network throughput in dynamic scenarios.

Contribution

It presents the first RL-based UAV positioning method that explicitly incorporates rate adaptation algorithms to enhance network throughput.

Findings

RARL achieves maximum throughput in static scenarios.

RARL adapts effectively to mobile scenarios.

Incorporating RA effects improves UAV positioning performance.

Abstract

With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios.