Algorithm 1. Kriging-aided process.
Designing the path loss:
1:	Tune the parameters $n$ and $e$ in (4) from the tuning dataset.
2:	Extract shadowing from (5).
3:	Generate shadowing trough the Kriging-aided process: variogram and interpolation.
Modeling the variogram:
4:	Calculate the experimental variogram.
5:	Summarize the experimental variogram.
6:	Fit a parametric curve.
7:	if variogram fits to the first 3 lags then
Kriging interpolation:
8:	Create the scenario through a grid.
9:	Ordinary Kriging constraint: ${\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{N}}{w}_{\mathrm{i}}=1$
10:	Calculate the weights $w_{\mathrm{i}}$ and the Lagrange multiplier.
11:	Estimate $L_{\mathrm{s}}$ trough the weighted average of the neighboring points.
Estimating the path loss:
12:	$L=L_{\mathrm{fs}}+10n{\log }_{10}\left(\frac{d}{d_0}\right)+e+L_{\mathrm{s}}$