Page 13 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 4 – AI and machine learning solutions in 5G and future networks

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 4

NetXplain: Real-time explainability of graph neural networks applied to
networking

Pages 57–66
David Pujol-Perich, José Suárez-Varela, Shihan Xiao, Bo Wu, Albert Cabellos-Aparicio,
Pere Barlet-Ros

Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle
complex optimization problems. However, existing DL-based solutions are often considered as black
boxes with high inner complexity. As a result, there is still certain skepticism among the networking
industry about their practical viability to operate data networks. In this context, explainability
techniques have recently emerged to unveil why DL models make each decision. This paper focuses on
the explainability of Graph Neural Networks (GNNs) applied to networking. GNNs are a novel DL
family with unique properties to generalize over graphs. As a result, they have shown unprecedented
performance to solve complex network optimization problems. This paper presents NetXplain, a novel
real-time explainability solution that uses a GNN to interpret the output produced by another GNN. In
the evaluation, we apply the proposed explainability method to RouteNet, a GNN model that predicts
end-to-end QoS metrics in networks. We show that NetXplain operates more than 3 orders of magnitude
faster than state-of-the-art explainability solutions when applied to networks up to 24 nodes, which
makes it compatible with real-time applications; while demonstrating strong capabilities to generalize
to network scenarios not seen during training.
View Article

Machine learning for performance prediction of channel bonding in next-
generation IEEE 802.11 WLANS

Pages 67–79
Francesc Wilhelmi, David Góez, Paola Soto, Ramon Vallés, Mohammad Alfaifi, Abdulrahman
Algunayah, Jorge Martín-Pérez, Luigi Girletti, Rajasekar Mohan, K Venkat Ramnan, Boris Bellalta
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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