Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912715696504832 |
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| author | Ethier, Jonathan Chateauvert, Mathieu Dempsey, Ryan G. Bose, Alexis |
| author_facet | Ethier, Jonathan Chateauvert, Mathieu Dempsey, Ryan G. Bose, Alexis |
| contents | Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historically been challenging, but geographic information systems data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and account for interference in wireless deployments. Machine learning-based modeling can significantly support this effort, with feature based approaches allowing for accurate, efficient, and scalable propagation modeling. Building on previous work, we introduce an extended set of features that improves prediction accuracy while, most importantly, proving model generalization through rigorous statistical assessment and the use of test set holdouts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_08306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction Ethier, Jonathan Chateauvert, Mathieu Dempsey, Ryan G. Bose, Alexis Machine Learning Signal Processing Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historically been challenging, but geographic information systems data is becoming increasingly available with higher resolution and accuracy. Access to such details enables propagation models to more accurately predict coverage and account for interference in wireless deployments. Machine learning-based modeling can significantly support this effort, with feature based approaches allowing for accurate, efficient, and scalable propagation modeling. Building on previous work, we introduce an extended set of features that improves prediction accuracy while, most importantly, proving model generalization through rigorous statistical assessment and the use of test set holdouts. |
| title | Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2501.08306 |