Estimating Rural Path Loss with ITU-R P.1812-7 : Impact of Geospatial Inputs

TL;DR

This study evaluates how different geospatial datasets influence the accuracy of the ITU-R P.1812-7 radio propagation model in rural areas, offering guidelines for better path loss estimation using available data.

Contribution

It systematically assesses the impact of various geospatial inputs on P.1812 model accuracy and provides practical recommendations for dataset integration in rural environments.

Findings

High-resolution data do not always improve accuracy.

Global datasets like GFCH are reliable when high-res data are unavailable.

Guidelines for integrating geospatial data into P.1812 are proposed.

Abstract

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and that global datasets such as the GFCH provide a robust alternative when high-resolution data are unavailable or out-of-date.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.