ATARI: A Graph Convolutional Neural Network Approach for Performance Prediction in Next-Generation WLANs

Abstract

IEEE 802.11 (Wi-Fi) is one of the technologies that provides high performance with a high density of connected devices to support emerging demanding services, such as virtual and augmented reality. However, in highly dense deployments, Wi-Fi performance is severely affected by interference. This problem is even worse in new standards, such as 802.11n/ac, where new features such as Channel Bonding (CB) are introduced to increase network capacity but at the cost of using wider spectrum channels. Finding the best channel assignment in dense deployments under dynamic environments with CB is challenging, given its combinatorial nature. Therefore, the use of analytical or system models to predict Wi-Fi performance after potential changes (e.g., dynamic channel selection with CB, and the deployment of new devices) are not suitable, due to either low accuracy or high computational cost. This paper presents a novel, data-driven approach to speed up this process, using a Graph Neural Network (GNN) model that exploits the information carried in the deployment’s topology and the intricate wireless interactions to predict Wi-Fi performance with high accuracy. The evaluation results show that preserving the graph structure in the learning process obtains a 64% increase versus a naive approach, and around 55% compared to other Machine Learning (ML) approaches when using all training features.

Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance With Machine Learning

“…While the accuracy achieved by the methods demonstrates the suitability of ML for predicting the throughput performance of complex WLANs, more importantly, this work can be easily extended by considering other approaches. The same dataset is used by Soto et al [164] to predict Wi-Fi performance using a GNN model that incorporates the deployment's topology information. Finally, the problem of collisions with hidden stations when channel bonding is used is described by Karmakar et al [160].…”

“…Experience replay is proposed as an alternative. • Further recommendations regarding training ML models include the following: the achieved model accuracy depends on the selected training features, using specific training data will lead to results which do not generalize, and, for limited training datasets, the subset selection becomes crucial [164], [249], [384]. Given the relevance of experience to avoid repeating mistakes, we encourage researchers to always include in their works an explicit 'lessons learned' section detailing new insights, or corroborating existing ones, to contribute to the development of this research area.…”

Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs

“…To the best of our knowledge, this approach has not been studied before in the context of SR. A centralized DL-based method was proposed in [25] to jointly select the transmission power and the CCA, but not in the context of 11ax SR operation. DL was also applied in [26] to address the channel bonding problem in dense WLANs. This and other DL solutions for the dynamic channel bonding problem in IEEE 802.11ax WLANs were overviewed in [27].…”

Federated spatial reuse optimization in next-generation decentralized IEEE 802.11 WLANS

“…To the best of our knowledge, this approach has not been studied before in the context of SR. A centralized DL-based method was proposed in [33] to jointly select the transmission power and the CCA, but not in the context of 11ax SR oper-ation. DL was also applied in [34] to address the channel bonding problem in dense WLANs. This and other DL so-lutions for the dynamic channel bonding problem in IEEE 802.11ax WLANs were reviewed in [35].…”