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Prediction Characteristics of Quasi-Moment-Method Calibrated Pathloss Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 37 |
| Year of Publication: 2020 |
| Authors: Michael Adedosu Adelabu, Ayotunde Ayorinde, A. Ike Mowete |
10.5120/ijca2020920939
|
Michael Adedosu Adelabu, Ayotunde Ayorinde, A. Ike Mowete . Prediction Characteristics of Quasi-Moment-Method Calibrated Pathloss Models. International Journal of Computer Applications. 175, 37 ( Dec 2020), 21-30. DOI=10.5120/ijca2020920939
Abstract
This paper investigates the pathloss prediction characteristics of basic models, subjected to calibration with the use of a novel technique, here referred to as the Quasi-Moment-Method, QMM. After a succinct description of the QMM calibration process, the paper presents computational results involving the calibration of three different basic models-the SUI, ECC33, and Ericsson models. The results reveal that the QMM typically reduces mean prediction (MP) and root mean square (RMS) errors by several tens of decibels. One other novelty introduced by the paper, is a comparison of contributions to total predicted pathloss, by components of the basic models, and their corresponding QMM-calibrated versions.
References
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