Development of a Canada-Wide Morphology Map for the ITU-R P. 1411 Propagation Model

TL;DR

This paper presents a machine learning-based methodology to create a comprehensive Canada-wide morphology map, improving the accuracy of outdoor propagation models across a broad frequency range.

Contribution

It introduces an automated classification approach for environment types, enhancing the precision of the ITU-R P.1411 propagation model for Canada.

Findings

Achieved high classification accuracy for environment types

Produced a detailed Canada-wide morphology map

Enhanced path loss estimation accuracy

Abstract

This paper outlines the development of a Canada-wide morphology map classifying regions into residential, urban low-rise, and urban high-rise environments, following the ITU-R P.1411-12 propagation model guidelines. To address the qualitative nature of the environment-type descriptors found in the Recommendation, a machine learning approach is employed to automate the classification process. Extensive experimentation optimized classification accuracy, resulting in a Canada-wide morphology map that ensures more accurate path loss estimations for outdoor short-range propagation at frequencies ranging from 300 MHz to 100 GHz.