## 5 Conclusions  
This paper proposes a 5G mmWave path loss model to which a novel training method called multi-way Local Attentive Learning is applied. The 3D CNN-based path loss model is obtained by using 3D LAMS images which include three-dimensional morphological data. Since these data can provide important information about the radio propagation environment of dense urban scenarios, the 3D CNN model can give more accurate path loss prediction results. The proposed training method called multi-way Local Attentive Learning can further enhance the performance of the 3D CNN model, thanks to the improved mechanism of local attention provided by multi-way partitioning of a sample data set. The path loss model performance is significantly improved by applying multi-way local attentive learning to the existing 3D CNN-based path loss model training. The experimental results shows that while the 3D CNN-based path loss model can guarantee performance in urban areas, applying the proposed multi-way local attentive learning can provide much better performance than using only basic learning methods, or meta-learning-based methods. In addition, we compared the prediction performance in LoS and NLoS environments for five models, including the proposed model. In all five models, the prediction performance in the NLoS environment was lower than the prediction performance in the LoS environment, which can be seen as being influenced by the characteristics of the mmWave with high straightness. Nevertheless, among the five compared models, the proposed model had high prediction accuracy in both LoS and NLoS environments. The dataset partitioning method proposed in Algorithm 2 partitions the dataset and assigns a data attribute to each data partition. Data in these partitions are not guaranteed to be specific to the data attributes assigned to them. Therefore, in the follow-up study, for better learning, an algorithm that allows data specialized for a specific data attribute to form a partition in the data partitioning process will be studied. In the future, it is expected that a path loss model that can be applied universally in urban areas could be provided through experiment with various hyperparameters, model fine-tuning, and training high-quality and quantity data. In addition, through deep learning model compression, it will be easier to use in the field, while maintaining, or increasing, model performance. We consider mixing a deterministic model, such as the 3D ray tracing method, and 3D CNN-based model with multi-way local attentive learning in a hybrid method in dense urban cases to increase prediction performance. Other state-of-the-art training methods and deep learning technologies should be considered regarding 5G path loss modeling. Conceptualization, H.L. ; methodology, H.L. ; software, W.J. and H.K. ; validation, W.J., H.K. and H.L. ; formal analysis, H.L. ; investigation, W.J. and H.K. ; resources, W.J. and H.K. ; data curation, W.J. and H.K. ; writing--original draft preparation, W.J. and H.K. ; writing--review and editing, W.J., H.K. and H.L. ; visualization, W.J. and H.K. ; supervision, H.L. ; project administration, H.L. ; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the MSIT, under the National Program for Excellence in SW supervised by the IITP (No. 2017-0-00096). The code used to support the findings of this study is available at the following url; [[https://github.com/ChickenRushkKR/mmWave-Path-Loss-Modeling-for-5G-Communications-in-Dense-Urban-Scenarios](https://github.com/ChickenRushkKR/mmWave-Path-Loss-Modeling-for-5G-Communications-in-Dense-Urban-Scenarios)]([https://github.com/ChickenRushkKR/mmWave-Path-Loss-Modeling-for-5G-Communications-in-Dense-Urban-Scenarios](https://github.com/ChickenRushkKR/mmWave-Path-Loss-Modeling-for-5G-Communications-in-Dense-Urban-Scenarios)) (accessed on 20 May 2022).