English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the advent of Artificial Intelligence (AI)-empowered communications,industry, academia, and standardization organizations are progressing on thedefinition of mechanisms and procedures to address the increasing complexity offuture 5G and beyond communications. In this context, the InternationalTelecommunication Union (ITU) organized the first AI for 5G Challenge to bringindustry and academia together to introduce and solve representative problemsrelated to the application of Machine Learning (ML) to networks. In this paper,we present the results gathered from Problem Statement~13 (PS-013), organizedby Universitat Pompeu Fabra (UPF), which primary goal was predicting theperformance of next-generation Wireless Local Area Networks (WLANs) applyingChannel Bonding (CB) techniques. In particular, we overview the ML modelsproposed by participants (including Artificial Neural Networks, Graph NeuralNetworks, Random Forest regression, and gradient boosting) and analyze theirperformance on an open dataset generated using the IEEE 802.11ax-orientedKomondor network simulator. The accuracy achieved by the proposed methodsdemonstrates the suitability of ML for predicting the performance of WLANs.Moreover, we discuss the importance of abstracting WLAN interactions to achievebetter results, and we argue that there is certainly room for improvement inthroughput prediction through ML.

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