Rate Adaptation Aware Positioning for Flying Gateways Using Reinforcement Learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Pantaleão, G (Autor) Outra; Queirós, R (Autor) Outra; Fontes, H (Autor) Outra; Campos, R (Autor) FEUP

Ata de Conferência Internacional

Título: Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Páginas: 272-286

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-B25

DOI: 10.1007/978-3-031-57523-5_21

Abstract (EN): 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. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14