Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs

• URL: http://arxiv.org/abs/2105.14219v1
• Date: Sat, 29 May 2021 05:33:07 GMT
• Title: Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs
• Authors: Francesc Wilhelmi, David G\'oez, Paola Soto, Ramon Vall\'es, Mohammad Alfaifi, Abdulrahman Algunayah, Jorge Martin-P\'erez, Luigi Girletti, Rajasekar Mohan, K Venkat Ramnan, Boris Bellalta
• Abstract summary: We present the results gathered from Problem Statement13 (PS-013), organized by Universitat Pompeu Fabra (UPF) The primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques.
• Score: 1.0486135378491268
• License: http://creativecommons.org/licenses/by/4.0/
• 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), which primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we overview 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 dataset 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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