{"ID":2867111,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18933","arxiv_id":"2509.18933","title":"Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning","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.","short_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 techniq...","url_abs":"https://arxiv.org/abs/2509.18933","url_pdf":"https://arxiv.org/pdf/2509.18933v1","authors":"[\"Gabriele Formis\",\"Gianluca Cena\",\"Lukasz Wisniewski\",\"Stefano Scanzio\"]","published":"2025-09-23T12:52:01Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}