Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915509074657280 |
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| author | Formis, Gabriele Cena, Gianluca Wisniewski, Lukasz Scanzio, Stefano |
| author_facet | Formis, Gabriele Cena, Gianluca Wisniewski, Lukasz Scanzio, Stefano |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18933 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning Formis, Gabriele Cena, Gianluca Wisniewski, Lukasz Scanzio, Stefano Networking and Internet Architecture Artificial Intelligence Machine Learning 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. |
| title | Accurate and Efficient Prediction of Wi-Fi Link Quality Based on Machine Learning |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.18933 |