AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networks

Abstract

Enterprise Wi-Fi networks can greatly benefit from Artificial Intelligence and Machine Learning (AI/ML) thanks to their well-developed management and operation capabilities. At the same time, AI/ML-based traffic/load prediction is one of the most appealing data-driven solutions to improve the Wi-Fi experience, either through the enablement of autonomous operation or by boosting troubleshooting with forecasted network utilization. In this paper, we study the suitability and feasibility of adopting AI/ML-based load prediction in practical enterprise Wi-Fi networks. While leveraging AI/ML solutions can potentially contribute to optimizing Wi-Fi networks in terms of energy efficiency, performance, and reliability, their effective adoption is constrained to aspects like data availability and quality, computational capabilities, and energy consumption. Our results show that hardware-constrained AI/ML models can potentially predict network load with less than 20% average error and 3% 85th-percentile error, which constitutes a suitable input for proactively driving Wi-Fi network optimization.