Research Article
Rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning
@INPROCEEDINGS{10.1007/978-3-031-57523-5_21, author={Gabriella Pantale\"{a}o and R\^{u}ben Queir\^{o}s and H\^{e}lder Fontes and Rui Campos}, title={Rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning}, proceedings={Simulation Tools and Techniques. 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings}, proceedings_a={SIMUTOOLS}, year={2024}, month={4}, keywords={Aerial networks Rate Adaptation UAV positioning Deep Reinforcement Learning}, doi={10.1007/978-3-031-57523-5_21} }- Gabriella Pantaleão
Rúben Queirós
Hélder Fontes
Rui Campos
Year: 2024
Rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning
SIMUTOOLS
Springer
DOI: 10.1007/978-3-031-57523-5_21
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.
