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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.11708 |
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| _version_ | 1866916574654365696 |
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| author | Chateauvert, Mathieu Ethier, Jonathan Florea, Adrian |
| author_facet | Chateauvert, Mathieu Ethier, Jonathan Florea, Adrian |
| contents | Accurate radio wave propagation modeling is essential for effective spectrum management by regulators and network deployment by operators. This paper investigates the ITU-R P.1812-7 (P.1812) propagation model's reliance on geospatial inputs, particularly clutter information, to improve path loss estimation, with an emphasis on rural geographic regions. The research evaluates the impact of geospatial elevation and land cover datasets, including Global Forest Canopy Height (GFCH), European Space Agency WorldCover, and Natural Resources Canada LandCover, on P.1812 propagation model prediction accuracy. Results highlight the trade-offs between dataset resolution, geospatial data availability, and representative clutter height assignments. Simulations reveal that high-resolution data do not always yield better results and that global datasets such as the GFCH provide a robust alternative when high-resolution data are unavailable or out-of-date. This study provides a set of guidelines for geospatial dataset integration to enhance P.1812's rural path loss predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11708 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Rural Path Loss with ITU-R P.1812-7 : Impact of Geospatial Inputs Chateauvert, Mathieu Ethier, Jonathan Florea, Adrian Signal Processing Accurate radio wave propagation modeling is essential for effective spectrum management by regulators and network deployment by operators. This paper investigates the ITU-R P.1812-7 (P.1812) propagation model's reliance on geospatial inputs, particularly clutter information, to improve path loss estimation, with an emphasis on rural geographic regions. The research evaluates the impact of geospatial elevation and land cover datasets, including Global Forest Canopy Height (GFCH), European Space Agency WorldCover, and Natural Resources Canada LandCover, on P.1812 propagation model prediction accuracy. Results highlight the trade-offs between dataset resolution, geospatial data availability, and representative clutter height assignments. Simulations reveal that high-resolution data do not always yield better results and that global datasets such as the GFCH provide a robust alternative when high-resolution data are unavailable or out-of-date. This study provides a set of guidelines for geospatial dataset integration to enhance P.1812's rural path loss predictions. |
| title | Estimating Rural Path Loss with ITU-R P.1812-7 : Impact of Geospatial Inputs |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2501.11708 |