## 3 Dynamic Area Optimization Based on an NN Considering Intercell Relationships
### Communication Performance  
Herein, we evaluate the effectiveness of our method by comparing communication performance metrics between our proposed method and GA. Furthermore, the detailed techniques in our proposed method are compared. Specifically, we compare the NN learning cell separately (\"NN\"), the CC learning cell with the relationship (\"NN+CC\"), and the ensemble approach learning cooperatively (\"NN+CC+Ensemble\"). We aim to identify the significance of each technique by analyzing them separately. It should be noted that, for this comparison, the default state involves uniformly placed beams within the area. Initially, we compare the DL SINR heatmaps generated by each method for a user distribution to visually understand how the CC and the ensemble method affect the NN. The heatmaps are illustrated in Figure 10. The black dashed arrows depict the beam \begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{1}{c}{**Downlink**} & \multicolumn{1}{c}{**Uplink**} \\ \hline Frequency & & 900 MHz \\ Bandwidth & 18 MHz & 360 kHz \\ Propagation & & Free-space loss \\ Transmitter power & 43 dBm/ant. & 23 dBm/ant. \\ Transmitter Antenna gain & Design param. & \(-3\) dBi \\ Receiver Antenna gain & \(-3\) dBi & Design param. \\ Noise figure & 5 dB & 3 dB \\ Required SNR & 8 dB & 11 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Beam Parameters. \begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Value** \\ \hline Sector division unit [deg.] & 20 \\ No. of inputs & 18 \\ No. of hidden layers in NN & 4 \\ No. of nodes in each hidden layer & 64 \\ Activation function & ReLU \\ Learning rate & \(1\times 10^{-4}\) \\ No. of epochs & 100 \\ Mini-Batch size & 512 \\ L2 regularization coefficient & \(1\times 10^{-4}\) \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Learning Parameters. Figure 9: Example of user distributions. (**a**) Dense area around Tokyo. (**b**) Sparse area around Akita. directions predicted by each method. There is a notable reduction in SINR for the NN estimation, due to interference between the cells at the upper and left positions, as well as an uncovered area between the left and bottom-right cells. The addition of CC appears to solve the cell interval adjustment problems. However, the predicted beam solutions depend on the sequence of the CC. To select the best solution with the highest objective function value across all the solutions, the ensemble approach is employed. In Figure 10, the heatmap surrounded by the red box offers the highest objective function value and is chosen by the ensemble approach. The heatmap is comparable to the one generated by the GA beam solution. This indicates that, despite the variations caused by the learning sequences of the CCs, the ensemble approach identifies the suitable pattern that improves communication quality as much as the GA beam solution. Subsequently, we compare constraint violation ratios, which are the distribution ratios wherein the constraints are violated among all user distributions, for each method. Incidentally, the quality control detailed in Section 3 was not applied here because it would make the ratios zero, and we would not be able to evaluate the ratios after the control. Figure 11 illustrates the constraint violation ratios in the three-cell and six-cell configurations for the NN, NN+CC, and NN+CC+Ensemble. The ratios for each method are 46.7%, 7.9%, and 0.2%, respectively, in the three-cell configuration, and 78.5%, 32.8%, and 0%, respectively, in the six-cell configuration. These results indicate that NN+CC and NN+CC+Ensemble significantly reduce the constraint violation ratios in both configurations. This improvement is largely due to the incorporation of CC, which considers the intervals between neighboring cells, and mitigates interferences and uncovered areas. Furthermore, for the ensemble approach, the decline of the constraint violation ratio is greater than NN+CC.