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Machine learning for performance prediction of channel bonding in next-generation IEEE 802.11 WLANS
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Authors: Francesc Wilhelmi, David Góez, Paola Soto, Ramon Vallés, Mohammad Alfaifi, Abdulrahman Algunayah, Jorge Martín-Pérez, Luigi Girletti, Rajasekar Mohan, K Venkat Ramnan, Boris Bellalta
Status: Final
Date of publication: 5 August 2021
Published in: ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4 - AI and machine learning solutions in 5G and future networks, Pages 67-79
Article DOI : https://doi.org/10.52953/NBGS1213
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 | Abstract:
With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the First AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement 13 (PS-013), organized by Universitat Pompeu Fabra (UPF), whose primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we provide an overview of the ML models proposed by participants (including artificial neural networks, graph neural networks, random forest regression, and gradient boosting) and analyze their performance on an open data set generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML. |
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Keywords: Channel bonding, IEEE 802.11 WLAN, ITU Challenge, machine learning, network simulator
Rights: © International Telecommunication Union, available under the CC BY-NC-ND 3.0 IGO license.
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