# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 6 Conclusions  
Due to the characteristics of the wireless signals at the SHF and mmWave frequency bands compared with today's _sub_-6 GHz frequency regime, providing accurate and stable path loss prediction models is a challenging problem. Motivated by that, this work considered an extensive comparative analysis to evaluate the performance of the most widespread and used machine learning methods, namely the MLR, PR, RF, DT, SVR, KNN, ANN, and RNN-LSTM. The data adopted for this research were collected in a typical indoor corridor environment at 14, 18, and 22 GHz frequency bands. The input features used to train the models were carefully selected: the Tx-Rx separation distance, frequency, Tx height, and the AoA. To ensure reliable and stable results, we used a cross-validation technique to divide the data into seven folds of training and testing datasets and provide the minimum, average, and maximum results. Moreover, we utilized a hyperparameters tuning method to select the optimum hyperparameters of the model and avoid the time consumption of the manual selection. Furthermore, five performance metrics were applied to evaluate the models: R-squared, RMSE, MAPE, MSE, and the correlations coefficient. The main results obtained from this work reveal that all the adopted models (except the MLR) have accurate and stable performances in predicting the path loss for enclosed indoor environments, such as corridors. Moreover, as the comparison between the models, the best-fit models according to the minimum RMSE and high R-squared and correlation factor are the ANN and RNN-LSTM. Finally, this work shows that these ML-based models could be promising solutions with higher precision for predicting path loss for future indoor wireless communication networks. The future scope of this work will be to evaluate deep learning methods \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Models**} & \multicolumn{2}{c}{\(\mathbf{R^{2}}\)} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MAPE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**Corr**} \\ \hline  & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** \\ \hline MLR & 0.3580 & 0.4367 & 0.5379 & 4.9492 & 5.4129 & 5.9942 & 0.6272 & 0.7240 & 0.8212 & 24.4943 & 29.2993 & 35.9299 & 0.4983 & 0.5658 & 0.6692 \\ PMR & 0.5707 & 0.6344 & 0.7385 & 3.3198 & 4.0033 & 4.0034 & 0.5978 & 0.6429 & 0.7181 & 11.0212 & 13.024 & 16.0272 & 0.6098 & 0.6343 & 0.7044 \\ SVR & 0.7562 & 0.7732 & 0.7943 & 2.9204 & 3.2991 & 3.5713 & 0.1097 & 0.1443 & 0.1720 & 8.5288 & 10.8839 & 12.7539 & 0.7970 & 0.8264 & 0.8650 \\ DT & 0.6648 & 0.7298 & 0.7696 & 2.5871 & 2.9862 & 3.2512 & 0.1198 & 0.1443 & 0.1697 & 6.6935 & 8.9175 & 10.5706 & 0.6633 & 0.7250 & 0.8088 \\ RF & 0.8211 & 0.8512 & 0.9092 & 1.8355 & 2.1076 & 2.6163 & 0.1064 & 0.1101 & 0.1345 & 3.3690 & 4.4421 & 6.8452 & 0.8447 & 0.8780 & 0.9026 \\ KNN & 0.7937 & 0.8455 & 0.8882 & 2.1794 & 2.2420 & 2.8016 & 0.1009 & 0.1272 & 0.1324 & 4.7498 & 5.0264 & 7.8486 & 0.8419 & 0.8814 & 0.9150 \\ ANN & 0.8089 & 0.8482 & 0.9052 & 1.9763 & 2.0028 & 2.4527 & 0.9988 & 0.1049 & 0.1364 & 3.9056 & 4.0114 & 6.0158 & 0.8973 & 0.9224 & 0.9306 \\ LSTM & 0.7456 & 0.8240 & 0.8976 & 1.7334 & 2.0226 & 2.4280 & 0.0839 & 0.0998 & 0.1291 & 3.0049 & 4.0910 & 5.8954 & 0.8686 & 0.9278 & 0.9499 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics after removing the antenna height and the AoA from the input features of the models. Figure 10: Measured and predicted path loss and the prediction error of all the ML-based models. (**a**) Measured and predicted path loss; (**b**) Prediction error. and provide generalization techniques that will help estimate the path loss in complex environments without the need to conduct massive measurement campaigns. Conceptualization, M.K.E. and O.S. ; methodology, M.K.E. and O.S. ; software, M.K.E. and O.S. ; validation, M.K.E. and O.S. ; resources, M.K.E. and T.J.A. ; writing--original draft preparation, M.K.E. and O.S.