  Investigations in this paper focus on establishing the uniqueness properties
of the Quasi Moment Method (QMM) solution to the problem of calibrating nominal
radiowave propagation pathloss prediction models. Nominal (basic) prediction
models utilized for the investigations, were first subjected to QMM
calibrations with measurements from three different propagation scenarios.
Then, the nominal models were recast in forms suitable for Singular Value
Decomposition (SVD) calibration before being calibrated with both the SVD and
QMM algorithms. The prediction performances of the calibrated models as
evaluated in terms of Root Mean Square Prediction Error (RMSE), Mean Prediction
Error (MPE), and Grey Relational Grade Mean Absolute Percentage Error (GRG
MAPE) very clearly indicate that the uniqueness of QMM calibrations of basic
pathloss models is more readily observable, when the basic models are recast in
forms specific to SVD calibration. In the representative case of calibration
with indoor to outdoor measurements, RMSE values were recorded for QMM
calibrated nominal models as 5.2639dB for the ECC33 model, and 5.3218dB for the
other nominal models. Corresponding metrics for the alternative (rearranged)
nominal models emerged as 5.2663dB for the ECC33 model and 5.2591dB for the
other models. A similar general trend featured in the GRG MAPE metrics, which
for both SVD and QMM calibrations of all the alternative models, was recorded
as 0.9131, but differed slightly (between 0.9138 and 0.9196) for the QMM
calibration of the nominal models. The slight differences between these metrics
(due to computational round off approximations) confirm that when the
components of basic models are linearly independent, the QMM solution is
unique.
