Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning

• URL: http://arxiv.org/abs/2509.18933v1
• Date: Tue, 23 Sep 2025 12:52:01 GMT
• Title: Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning
• Authors: Gabriele Formis, Gianluca Cena, Lukasz Wisniewski, Stefano Scanzio,
• Abstract summary: The paper evaluates the performance of data-driven models based on the linear combination of exponential moving averages.<n>Channel-independent models, which allow for generalized training by equipment manufacturers, demonstrated competitive performance.<n>Overall, this study provides insights into the practical deployment of machine learning-based prediction models for enhancing Wi-Fi dependability in industrial environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wireless communications are characterized by their unpredictability, posing challenges for maintaining consistent communication quality. This paper presents a comprehensive analysis of various prediction models, with a focus on achieving accurate and efficient Wi-Fi link quality forecasts using machine learning techniques. Specifically, the paper evaluates the performance of data-driven models based on the linear combination of exponential moving averages, which are designed for low-complexity implementations and are then suitable for hardware platforms with limited processing resources. Accuracy of the proposed approaches was assessed using experimental data from a real-world Wi-Fi testbed, considering both channel-dependent and channel-independent training data. Remarkably, channel-independent models, which allow for generalized training by equipment manufacturers, demonstrated competitive performance. Overall, this study provides insights into the practical deployment of machine learning-based prediction models for enhancing Wi-Fi dependability in industrial environments.

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