Research Article
RateRL: A Framework for Developing RL-Based Rate Adaptation Algorithms in ns-3
@INPROCEEDINGS{10.1007/978-3-031-57523-5_22, author={Ruben Queiros and Lu\^{\i}s Ferreira and Helder Fontes and Rui Campos}, title={RateRL: A Framework for Developing RL-Based Rate Adaptation Algorithms in ns-3}, 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={Wireless Networks ns-3 Deep Reinforcement Learning Machine Learning Tool}, doi={10.1007/978-3-031-57523-5_22} }- Ruben Queiros
Luís Ferreira
Helder Fontes
Rui Campos
Year: 2024
RateRL: A Framework for Developing RL-Based Rate Adaptation Algorithms in ns-3
SIMUTOOLS
Springer
DOI: 10.1007/978-3-031-57523-5_22
Abstract
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.
