Development of a Canada-Wide Morphology Map for the ITU-R P. 1411 Propagation Model
This work addresses the need for precise propagation modeling in telecommunications, particularly for Canada, but is incremental as it applies existing methods to a new geographic dataset.
The paper tackled the problem of automating environment classification for propagation models by developing a Canada-wide morphology map using machine learning, resulting in more accurate path loss estimations for outdoor short-range propagation across frequencies from 300 MHz to 100 GHz.
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.