## 4 Experiments and Results
All the models, especially 3D CNN, tended to have lower absolute errors as the distance increased. In the case of the first section, the horizontal relative angle was small and the distance was close, but because the distance was small, the influence of the vertical relative angle increased, and it seemed that the absolute error occurred in the techniques that were not used for learning. Figure 6 is the same as Figure 5, except that NLoS was used instead of the LoS test dataset. Among the test data, NLoS did not have test data at 180 m to 190 m, 200 m to 240 m, so there were no dots on the graph. Figure 6 shows that the results of all five models were similar, but this is because the number of data allocated for each section was different. As the distance was shorter, even if there were only a small number of buildings between Tx-Rx, the probability of being obscured by the buildings increased, so that a lot of data could be scattered in the short distance. However, the data we used for the test tended to be concentrated at 60-70 m, 120-130 m, and 170-180 m. In the 120-130 m and 170-180 m sections where the absolute error of 3D CNN with multi-way local attentive learning--single model was the lowest, there were many times more data than other sections, so, as in Table 6, the Single 3D CNN model had the lowest error rate. Table 7 shows the RMSE and the Cumulative distribution function (CDF) of absolute error based on 1, 3, and 10 dBm, respectively, for the entire test dataset, regardless of LoS/NLoS. Figure 7 is a graph that expressed by sorting the receive points of the entire test data set in order of distance, dividing the sections at intervals of 10 m, and averaging the absolute errors of various experiments included in the section. When looking at each section of the T-R segment, the absolute error of the multi-way local attentive learning--single model in most sections was ranked low among the five path loss models. From Table 7 and Figure 7, it could be confirmed that the Single 3D CNN model had the lowest RMSE and absolute error among the five models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**LoS/NLoS**} & \multicolumn{4}{c}{**RMSE (dBm)**} \\ \cline{3-5}  & & **Minimum** & **Mean** & **Maximum** \\ \hline \multirow{2}{*}{Single 3D CNN model} & LoS & 3.15 & 4.38 & 8.01 \\  & NLoS & 4.06 & 7.55 & 9.45 \\ \hline \multirow{2}{*}{Multiple 3D CNN model} & LoS & 3.11 & 4.85 & 11.23 \\  & NLoS & 4.10 & 7.73 & 14.08 \\ \hline \multirow{2}{*}{3D CNN model} & LoS & 4.86 & 7.63 & 11.57 \\  & NLoS & 5.93 & 8.11 & 16.46 \\ \hline \multirow{2}{*}{3D CNN with Meta-learning} & LoS & 3.98 & 7.25 & 9.62 \\  & NLoS & 4.52 & 8.85 & 22.12 \\ \hline \multirow{2}{*}{Aster propagation model} & LoS & 7.23 & 8.89 & 9.92 \\  & NLoS & 9.43 & 11.04 & 14.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Minimum, Mean, Maximum Comparison Root Mean Square Error of various methods (LoS/NLoS). Figure 5: Absolute Error Comparison—LoS. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**RMSE (dBm)**} & \multicolumn{3}{c}{**Absolute Error CDF (\%)**} \\ \cline{3-5}  & & **\textless{1 dBm}** & **\textless{3 dBm}** & **\textless{10 dBm}** \\ \hline Single 3D CNN model & 6.01 & 15.46 & 46.91 & 90.21 \\ Multiple 3D CNN model & 6.67 & 12.37 & 36.61 & 77.32 \\
3D CNN & 7.85 & 7.21 & 22.16 & 77.84 \\
3D CNN with Meta-learning & 8.01 & 11.34 & 27.84 & 78.87 \\ Aster propagation model & 9.91 & 9.28 & 30.41 & 70.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison Root Mean Square Error of various methods. Figure 6: Absolute Error Comparison—NLoS. Table 7 and Figure 8 show the Cumulative Distribution Function for the absolute error of labels and predicted values for the path loss regression problem. First, as can be seen from Figure 8, the absolute error distribution of the 3D CNN-based path loss model was generally lower than the absolute error distribution of the Aster propagation model.