RateRL: A Framework for Developing RL-Based Rate Adaptation Algorithms in ns-3

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

Autores

Queirós, R (Autor) Outra; Ferreira, L (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: 287-298

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-010-ERY

DOI: 10.1007/978-3-031-57523-5_22

Abstract (EN): The increasing complexity of recent Wi-Fi amendments is making the use of traditional algorithms and heuristics unfeasible to address the Rate Adaptation (RA) problem. This is due to the large combination of configuration parameters along with the high variability of the wireless channel. Recently, several works have proposed the usage of Reinforcement Learning (RL) techniques to address the problem. However, the proposed solutions lack sufficient technical explanation. Also, the lack of standard frameworks enabling the reproducibility of results and the limited availability of source code, makes the fair comparison with state of the art approaches a challenge. This paper proposes a framework, named RateRL, that integrates state of the art libraries with the well-known Network Simulator 3 (ns-3) to enable the implementation and evaluation of RL-based RA algorithms. To the best of our knowledge, RateRL is the first tool available to assist researchers during the implementation, validation and evaluation phases of RL-based RA algorithms and enable the fair comparison between competing algorithms.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11