## 3 Dynamic Area Optimization Based on an NN Considering Intercell Relationships
### Cost Evaluation  
Here, we evaluate several factors related to calculation costs caused by our proposed method, including the number of models required for the ensemble approach, amount of training data, and total computation time. First, our ensemble approach involves all permutations of the CC models. However, as the number of cells increases, the number of possible model permutations grows exponentially, increasing the computational cost. Therefore, we evaluate the number of models necessary for the ensemble. Second, because the amount of training data directly impacts the training time and costs, we evaluate the amount of training data required to accomplish throughput performance comparable to that of GA. Finally, we compare the total time required for optimizing each user distribution, which includes both the training and prediction times, to that of GA to determine the efficiency of the proposed method. Figure 14 illustrates the relationship between the number of CC models employed in the ensemble approach and the median of total throughput for the six-cell configuration. The models were randomly selected from the permutations of the six cells for reducing the number of models, and the average of the total throughput medians was calculated. As a side note, this evaluation was not carried out for the three-cell configuration owing to the small number of permutations; thus, no reduction was necessary. Conversely, the six-cell configuration required reduction owing to the large number of permutations. As depicted in Figure 14, the median of the total throughput decreases with the reduction in the number of models. In particular, when the number of models reduces to 100 or less, the median of the total throughput falls drastically. Specifically, it is 13.2% for the default state, whereas it is 0.38%, 0.84%, 1.11%, 2.69%, and 8.10% when employing 720, 200, 100, 10, and 1 models, respectively. This implies that even with a certain degree of model reduction, the degradation ratios remain small. This can be attributed to the similar  
Figure 13: Cumulative distribution curves of the errors for the beam directions, averaged for each cell. (**a**) Three-cell configuration. (**b**) Six-cell configuration. values obtained from permutations of CC estimations. The CC models trained in similar sequences generate similar outputs. The result suggests that, by allowing a certain level of degradation, the number of models can be pruned. For example, accepting a degradation tolerance of roughly 1.1% allows a reduction in the number of models by 1/7 (roughly 100 out of 720). Figure 15 illustrates the relationship between the amount of training data and the total throughput median for the six-cell configuration. To evaluate the required amount of training data for this problem, the amount of training data varying from 4000 to 36,000 is exclusively utilized for this evaluation. This figure includes results from NN, NN+CC, and NN+CC+Ensemble with 720 models, as well as NN+CC+Ensemble with 100 models selected randomly. It can be observed that the total throughput medians for NN+CC+Ensemble with 720 and 100 models converge from 16,000 data points, reaching 498 and 496 Mbps, respectively. Therefore, 16,000 data points are considered sufficient for training in this study. This convergence can be attributed to the increased generalizability of the models as the patterns in the training data expand, thereby improving adaptability to the test data. Conversely, it can also be observed that increasing the amount of training data does not significantly impact the performance of the NN. This is primarily attributed to insufficient information for learning. The information from adjacent cells is essential, and the integration of this information into the input through the NN+CC enhances performance. Figure 16 shows the relationship between the number of optimized distributions and the total time required for optimizing each user distribution for GA and the proposed method with an ensemble of 720 and 100 models.