On the Response of Basic Walfisch-Ikegami and Walfisch-Bertoni Models to QMM Calibration

TL;DR

This study evaluates how well basic Walfisch-Ikegami and Walfisch-Bertoni models respond to QMM calibration, showing improved accuracy and detailed component analysis using measurement data.

Contribution

It demonstrates the suitability of model parameters for QMM calibration and compares the calibration performance of different Walfisch models with detailed component analysis.

Findings

Walfisch Bertoni model has better RMSE responses than Walfisch-Ikegami models.

All QMM calibrated models exhibit excellent mean prediction error (<0.001 dB).

Component parameters effectively serve as expansion or testing functions for calibration.

Abstract

This paper systematically examines the response to Quasi Moment Method (QMM) calibration, of the basic COST231 Walfisch Ikegami, ITUR Walfisch Ikegami, and Walfisch-Bertoni models. First, it is demonstrated that the component parameters of the models are suitable candidates for use as expansion or testing functions with QMM pathloss model calibration schemes; and thereafter, the basic models are subjected to calibration, using measurement data available in the open literature. Computational results reveal that the COST231 Walfisch Ikegami and ITU Walfisch Ikegami models have virtually identical QMM calibration root mean square error (RMSE) responses; and that the Walfisch Bertoni model has better RMSE responses than both of them. A particular attribute revealed by the simulation results is that all QMM calibrated Walfisch type basic models have excellent mean prediction error (MPE) metrics (in general, less than 0.001dB). In addition to pathloss prediction profiles, the paper also presents profiles of disaggregated net pathloss, in terms of contributions by component parameters, including roof-top-to-street diffraction and scatter loss and multiscreen diffraction loss.