## 4 Experiments and Results
The advantage of meta-learning is that, given a new environment, it can learn faster and produce good generalization performance compared to before, and it can learn efficiently with less data. In the case of path loss model learning, meta-learning-based learning consisting of a number of meta-learning tasks based on the actual measurement area (base station), base station operation morphology, and Tx-Rx distance could be applied [22]. The Aster propagation model was based on advanced ray-tracing propagation techniques and combined high accuracy. The single and multiple 3D CNN models with multi-way local attentive learning, the vanilla 3D CNN model, and the 3D CNN model with meta-learning were trained and tested for comparison. The Aster propagation model included in Atoll was also used to generate predicted path loss values. Table 5 compares these models. Table 6 shows the mean, minimum, and maximum values for the entire test data of various models mentioned above. Analysis of LoS in Table 6 first shows that the Single 3D CNN model showed the best performance. Its minimum, mean and maximum were all lowest compared to other models, especially its mean, which was 4.38 dBm, with a difference of 0.47 dBm, compared to 4.85 dBm, the second lowest mean of Multiple 3D CNN model. The difference between its minimum and maximum was 4.86 dBm, the second smallest after 2.69 dBm of the Aster propagation model. This confirmed that 3D CNN with multi-way local attentive learning--single model had the second smallest variation among the five models. By comparing the minimum, mean, maximum, and variation of the aforementioned models, it could be seen that in LoS, the performance was good in the order of Single 3D CNN model, followed by Multiple 3D CNN model, 3D CNN with Meta-learning, basic 3D CNN, and Aster propagation model. Among them, the RMSE gap between the two 3D CNNs with multi-way local attentive learning was very small compared to the gap with other models. In the case of mean, it was very small, about 0.47 dBm. Next, analyzing NLoS also shows that the Single 3D CNN model performed best. Its minimum, mean, and maximum all showed the lowest values compared to other models, but the difference from other models was smaller than in LoS. Its mean was 7.55 dBm, which was about 0.18 dBm different from 7.73 dBm, which was that of the Multiple 3D CNN model. In NLoS, the variation of 3D CNN with multi-way local attentive learning was also the smallest. Model performance was in the order of variation of Single 3D CNN model, multi model, basic 3D CNN, 3D CNN with Meta-learning, and Aster propagation model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Minibatch Schemes** \\ \hline Single 3D CNN model & Attribute-Based \\ Multiple 3D CNN model & Attribute-Based \\
3D CNN & Random Sampling \\
3D CNN with Meta-learning & Task-Based \\ Aster propagation model & None \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the proposed work with previous related works. Through the analysis of Table 6, it can be seen that 3D CNN with multi-way local attentive learning had improved both LoS and NLoS, compared to the basic 3D CNN, which was from a previous study. Of course, although the increase in performance improvement in NLoS was smaller than the increase in LoS, the improvement in performance in NLoS, from which it is more difficult to predict path loss, was meaningful. Figure 5 is a graph that is expressed by sorting the receive points of the LoS 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. It helps enable more detailed analysis by comparing the measured errors for each section divided by distance. Figure 5 shows that 3D CNN with multi-way local attentive learning--single model had a lower absolute error in sections in front of 150 m, compared to the 3D CNN and 3D Aster propagation models and in sections behind 150 m, compared to all the rest of the models.