## 4 Experiments and Results  
The field measurement dataset has a total of 750 measured RSRP values. Each piece of measured data has corresponding GPS-based location information (longitude, latitude, and height) and serving physical cell ID (PCI). This measured scenario consists of seven PCIs, which cover an area of approximately 300 m wide and 300 m long. The number of pieces of measurement data allocated for each PCI is different. This measured dataset was collected by driving vehicles equipped with GPS receivers and 5G mmWave RSRP measurement devices. The transmitter also consisted of different locations (longitude, latitude, and height) and different settings (Azimuth, Downtilt). The attributes of the measured data and transmitter data are in Table 3, and the RSRP intensity of the measured data and the location plots of the PCI are in Figure 4. We also found that two additional attributes, i.e., the distance (T-R separation) and the relative angle, affected the RSRP values the most through correlation analysis. As illustrated in Figure 4, the two parameters, the horizontal relative angle and the distance from Tx to Rx, have a great influence on the RSRP value. We used these attributes mainly in multi-way partitioning. The Line-of-Sight (LoS) is an important factor that greatly affects the path loss. Since it is difficult for a deep learning model to extract LoS/NLoS information directly from a 3D map, preprocessing is needed to provide an explicit LoS/NLoS value at each location. This can be done simply by calculating the ratio of virtual Tx-Rx line segment traversing through building structures. If the ratio is close to 0%, the Tx-Rx pair is determined as 'LoS'. The goal of this study was to predict the measured RSRP value by the proposed model when there were measured data and transmitter information which were not used for training. The dataset had 750 measured data mapped to 7 PCIs, respectively, and a scenario with a train-to-test ratio of 75:25 and an LoS environment ratio of nearly half could be selected, as shown in Table 4. The finally selected scenario consisted of data corresponding to PCI 1 and 3 as test data and the remaining data as training data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Test PCI** & **TS Ratio (\%)** & **LoS Ratio (\%)** \\ \hline
1 & \(1,2\) & 30.80 & 68 \\
2 & \(1,3\) & 25.87 & 55 \\
3 & \(1,4\) & 31.33 & 60 \\
4 & \(2,3\) & 24.13 & 30 \\
5 & \(2,4\) & 29.60 & 39 \\
6 & \(3,4\) & 24.67 & 22 \\
7 & \(1,6,7\) & 25.60 & 56 \\
8 & \(2,6,7\) & 23.86 & 30 \\
9 & \(4,6,7\) & 24.40 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected scenario information. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Rx** & **Tx** \\ \hline \multirow{6}{*}{Attributes} & Date and Time & PCI \\  & Longitude & Longitude \\  & Latitude & Latitude \\  & GPS Height & Height \\  & GPS Speed & Azimuth \\  & GPS Satellite & Mechanical downtilt \\  & GPS Head & \\  & NR Serving PCI & \\  & RSRP (dBm) & \\ \hline count & 750 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The organization of measurement data. Figure 4: Measured data on map. Cyan color circles indicate PCIs and the arrow of PCI indicates the Azimuth setting itself. The circles on the road represent RSRP values measured by the receiver at each location. The structure of the 3D CNN model was the same as that mentioned in Section 3.2, and the multi-way local attentive learning technique mentioned in Section 3.3 was applied to the model. For comparison, prediction results of the basic 3D CNN, 3D CNN with meta-learning, and the Aster propagation model of Atoll were used in addition to the 3D CNN model with multi-way local attentive learning applied [20]. The 3D CNN model with meta-learning was applied to 3D CNN models for generalization of a generic model that accommodated different set values of each transmitting antenna, and different location characteristics of the received Rx, such as LoS/NLoS propagation.