## 7 Conclusions  
By applying deep learning methodology, this paper has presented a path loss model based on a profile along the direct propagation path between Tx-Rx pairs in urban environments for 5G cellular communication systems. The neural network of the proposed model extracts important features and then predicts the path loss based upon them. Simulation results showed that the proposed model outperforms conventional models. The standard deviation of prediction error was reduced by 34% when compared to the conventional models. In addition to pursuing the prediction performance empowered by the nonlinear mapping of the deep learning method, this paper has also explored the internal mechanism of the neural network. Eight explainable features were tested and used to predict the path loss, based on a linear regression approach. The simulation results showed that the accuracy of the explainable model reached 72%. Furthermore, the proposed deep learning model was also evaluated in a non-standalone 5G NR network in urban Taipei City. The measurements show that the standard deviation of prediction error was reduced by 30-43% when compared to the conventional models, and that the accuracy of the explanatory model reached 63% in the realistic 5G network.  
This research was funded by the Ministry of Science and Technology (MOST), Taiwan, grant number MOST 109-2222-E-035-003-MY2. Not applicable. Not applicable.  
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