Extending Machine Learning Based RF Coverage Predictions to 3D
Fuente:
arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866914931514802176 |
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| author | Chen, Muyao Châteauvert, Mathieu Ethier, Jonathan |
| author_facet | Chen, Muyao Châteauvert, Mathieu Ethier, Jonathan |
| contents | This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train models that provide power estimates with both good accuracy and with real-time simulation speeds. Work involving improved training data pre-processing as well as 3D predictions with arbitrary transmitter height is discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00050 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extending Machine Learning Based RF Coverage Predictions to 3D Chen, Muyao Châteauvert, Mathieu Ethier, Jonathan Signal Processing Computer Vision and Pattern Recognition Information Theory Machine Learning This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train models that provide power estimates with both good accuracy and with real-time simulation speeds. Work involving improved training data pre-processing as well as 3D predictions with arbitrary transmitter height is discussed. |
| title | Extending Machine Learning Based RF Coverage Predictions to 3D |
| topic | Signal Processing Computer Vision and Pattern Recognition Information Theory Machine Learning |
| url | https://arxiv.org/abs/2409.00050 |