## 5 Conclusions and Future Work  
In this study, we have presented an NN-based dynamic area optimization method that matches the performance of a GA. The method is easy to implement and allows for instant adaptation of the system in response to shifts in the user distribution. With the utilization of NN for dynamic area optimization, problems such as interference or uncovered areas between cells may arise due to incorrect beam intervals, which prevent sufficient communication capacity improvement. To account for adjacent cell relationships during training, we integrate a CC into the NN. Nevertheless, a CC might face learning order challenges depending on the user distribution, which causes performance fluctuations. Thus, our proposed method employs an ensemble approach to integrate multiple CC variations and choose the pattern best suited for the objective function. The simulation results demonstrate notable improvements in the optimization of horizontal beam directions for both three- and six-cell configurations. In addition, the number of models used in the ensemble approach can be scaled down by allowing a certain level of performance reduction. Further, the time efficiency of the proposed method exceeds that of GA because the training time is relatively short and the prediction speed is high.  
This study's evaluation is solely based on the results learned and predicted from the data we prepared. It is important to highlight that there may still be challenges when it comes to real-world applications. For instance, if the dataset changes, the amount of learning data necessary for accurate results could increase, and performance may vary. To tackle this, we could consider gathering broader and more diverse learning data to improve the model's robustness. Recent advancements like transfer learning and federated learning could also be beneficial. Transfer learning can adjust the model to specific areas, while federated learning can increase flexibility by merging models trained in different areas. These methods might enhance our approach.  
Conceptualization, W.T.; methodology, W.T. and Y.S.; validation, W.T. and Y.S. and K.H.; formal analysis, W.T. and Y.S.; writing--original draft preparation, W.T. and Y.S.; writing--review and editing, W.T., Y.S., K.H. and T.O. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Institute of Information and Communications Technology (NICT) in Japan under Grant Number JPJ012368C05701.  
The datasets presented in this article are not available because the data was obtained from user devices affiliated with our company and the public disclosure of this information is restricted.  
[PERSON], [PERSON], [PERSON] are currently employed at the SoftBank Corp. Technology Research Laboratory. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.