Home > Published Issues > 2023 > Volume 14, No. 6, 2023 >
JAIT 2023 Vol.14(6): 1198-1205
doi: 10.12720/jait.14.6.1198-1205

Throughput Prediction in Dense IEEE 802.11 WLANs Using Graph Neural Networks

Rajasekar Mohan *, Aman Cyrano Dsouza, P. Punith, and J. Manikandan
Electronics & Communication Engineering Department, RR Campus, PES University, Bangalore, India;
Email: amanjosephite@gmail.com (A.C.D.), punithprakash2001@gmail.com (P.P.), manikandanj@pes.edu (J.M.)
*Correspondence: rajasekarmohan@pes.edu (R.J.)

Manuscript received March 6, 2023; revised April 23, 2023; accepted June 13, 2023; published November 10, 2023.

Abstract—With the growing adaptation of Wi-Fi and the increased possibilities of complementing it with 5G, there is a need to exploit the fullest potential of the IEEE 802.11ac/ax and higher Wireless Local Area Network (WLAN) standards, especially in densely deployed scenarios. Machine learning techniques can be used to predict the performance of WLANs with the vast training data obtained through contemporary network simulators. They are quite useful to predict the throughput in crowded and dynamic deployments of WLANs where hand-crafted solutions may not be feasible. This paper presents a novel, data-driven approach that can contribute to improving the performance of next-generation WLANs. In particular, we employ a Graph Neural Network (GNN) model to predict the performance of Wi-Fi deployments by exploiting topology information and capturing complex wireless interactions. The network simulator, Komondor, is used to simulate different real-life scenarios for generating comprehensive datasets for training the model. Our approach addresses challenges related to energy efficiency, latency, and data rate in WLANs, and the regression model can be used to predict the throughput of a Basic Service Set (BSS) before it is deployed, allowing for better network design and optimization. The findings of this study demonstrated that GNNs can accurately forecast the throughput of BSSs in WLAN deployments in a given region with minimal information. Overall, our proposed approach can significantly influence the choice of topology for deployment, leading to optimal performance in crowded and dynamic WLAN scenarios.
Keywords—IEEE 802.11ac/ax, Overlapping Basic Service Set (OBSS), Komondor, performance prediction, throughput, next-generation Wireless Local Area Networks (WLANs), Graph Neural Network (GNN), International Telecommunication Union (ITU) challenge

Cite: Rajasekar Mohan, Aman Cyrano Dsouza, P. Punith, and J. Manikandan, "Throughput Prediction in Dense IEEE 802.11 WLANs Using Graph Neural Networks," Journal of Advances in Information Technology, Vol. 14, No. 6, pp. 1198-1205, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.