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.

企业 Wi-Fi 网络可以通过人工智能和机器学习（AI/ML）的管理和操作能力大大受益。同时，基于 AI/ML 的流量 / 负载预测是改善 Wi-Fi 体验最具吸引力的数据驱动解决方案之一。本文研究了在实际企业 Wi-Fi 网络中采用基于 AI/ML 的负载预测的适用性和可行性。我们的结果表明，受硬件限制的 AI/ML 模型可以对网络负载进行预测，平均误差不超过 20％，85 区位数误差不超过 3％，这为主动驱动 Wi-Fi 网络优化提供了适当的输入。