AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networks
This addresses network optimization for enterprise Wi-Fi users, but it is incremental as it applies existing AI/ML methods to a specific domain.
The paper tackled the problem of load prediction in enterprise Wi-Fi networks using AI/ML, showing that hardware-constrained models can predict network load with less than 20% average error and 3% 85th-percentile error.
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.