Singular Value Decomposition and Quasi-Moment-Method as Pathloss Model Calibration Alternatives

Abstract

Using indoor-to-outdoor pathloss measurements for a femtocell network, this paper presents a comparative evaluation of the performances of Singular Value Decomposition (SVD) and Quasi-Moment-Method (QMM) as pathloss model calibration tools. First, the performances of two published SVD models are compared with those of corresponding QMM models, developed through the calibration of basic ECC33 and WINNER II models. Then, and after noting that the ‘base models’ from which the poorer performing, published SVD calibrations reportedly derive, are either incompletely described or characterized by misprints, alternative ‘base models’ are prescribed by this paper.  It is then shown through analysis that QMM and SVD represent alternative implementations of the same basic model calibration algorithm. Computational results due to the alternative models suggest that better performance metrics (Mean Prediction Error (MPE) and Root Mean Square Prediction Error (RMSPE)) are recorded, when existing basic models are modified to mimic the SVD ‘base model’, prior to SVD/QMM calibration. Indeed, because the MPE due to the calibration of the alternative models are all close to zero (actually equal to zero in a few cases), the associated residual profiles closely follow the Gaussian distribution typically assumed in the literature, for shadow fading modelling.