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

arXiv: https://arxiv.org/abs/2105.14219 · HTML: https://mlsanity.com/paper/2105.14219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 12 of 12)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [DeepWiFi: Cognitive WiFi with Deep Learning](https://mlsanity.com/paper/1910.13315/) (2019)
- [Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs](https://mlsanity.com/paper/1805.11083/) (2018)
- [Komondor: a Wireless Network Simulator for Next-Generation High-Density WLANs](https://mlsanity.com/paper/1811.12397/) (2018)
- [IEEE 802.11ax: High-Efficiency WLANs](https://mlsanity.com/paper/1501.01496/) (2015)
- [Spatial Reuse in IEEE 802.11ax WLANs](https://mlsanity.com/paper/1907.04141/) (2019)
- [A Flexible Machine Learning-Aware Architecture for Future WLANs](https://mlsanity.com/paper/1910.03510/) (2019)
- [Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks](https://mlsanity.com/paper/2005.08281/) (2020)
- [Throughput Analysis in CSMA/CA Networks using Continuous Time Markov Networks: A Tutorial](https://mlsanity.com/paper/1404.0180/) (2014)
- [To overlap or not to overlap: Enabling Channel Bonding in High-Density WLANs](https://mlsanity.com/paper/1803.09112/) (2018)

## Built on by (most cited first, 1 of 1)

- [Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs](https://mlsanity.com/paper/2203.10472/) (2022)

## Abstract (arXiv)

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.