Improving Wi-Fi Network Performance Prediction with Deep Learning Models
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915650123857920 |
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| author | Formis, Gabriele Ericson, Amanda Forsstrom, Stefan Thar, Kyi Cena, Gianluca Scanzio, Stefano |
| author_facet | Formis, Gabriele Ericson, Amanda Forsstrom, Stefan Thar, Kyi Cena, Gianluca Scanzio, Stefano |
| contents | The increasing need for robustness, reliability, and determinism in wireless networks for industrial and mission-critical applications is the driver for the growth of new innovative methods. The study presented in this work makes use of machine learning techniques to predict channel quality in a Wi-Fi network in terms of the frame delivery ratio. Predictions can be used proactively to adjust communication parameters at runtime and optimize network operations for industrial applications. Methods including convolutional neural networks and long short-term memory were analyzed on datasets acquired from a real Wi-Fi setup across multiple channels. The models were compared in terms of prediction accuracy and computational complexity. Results show that the frame delivery ratio can be reliably predicted, and convolutional neural networks, although slightly less effective than other models, are more efficient in terms of CPU usage and memory consumption. This enhances the model's usability on embedded and industrial systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11168 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Wi-Fi Network Performance Prediction with Deep Learning Models Formis, Gabriele Ericson, Amanda Forsstrom, Stefan Thar, Kyi Cena, Gianluca Scanzio, Stefano Networking and Internet Architecture Artificial Intelligence Machine Learning Signal Processing The increasing need for robustness, reliability, and determinism in wireless networks for industrial and mission-critical applications is the driver for the growth of new innovative methods. The study presented in this work makes use of machine learning techniques to predict channel quality in a Wi-Fi network in terms of the frame delivery ratio. Predictions can be used proactively to adjust communication parameters at runtime and optimize network operations for industrial applications. Methods including convolutional neural networks and long short-term memory were analyzed on datasets acquired from a real Wi-Fi setup across multiple channels. The models were compared in terms of prediction accuracy and computational complexity. Results show that the frame delivery ratio can be reliably predicted, and convolutional neural networks, although slightly less effective than other models, are more efficient in terms of CPU usage and memory consumption. This enhances the model's usability on embedded and industrial systems. |
| title | Improving Wi-Fi Network Performance Prediction with Deep Learning Models |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2507.11168 |