Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914304557580288 |
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| author | Dempsey, Ryan G. Ethier, Jonathan Yanikomeroglu, Halim |
| author_facet | Dempsey, Ryan G. Ethier, Jonathan Yanikomeroglu, Halim |
| contents | Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17752 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks Dempsey, Ryan G. Ethier, Jonathan Yanikomeroglu, Halim Signal Processing Machine Learning Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics. |
| title | Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2411.17752 |