# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 5 Results and Discussions  
Figure 6 depicts the measured data and ML-based path loss prediction models for the enclosed indoor wireless channel selected in this work. These results are for 20% of the measurement data used for testing the performance of the models. As described above, the input features of all the ML-based models adopted for this study are the Tx-Rx distance, operating frequency, AoA, and the Tx antenna height. From Figure 6, all the models (except the MLR) fit the real measurement data accurately. Furthermore, it is clear from the figure that there is a significant match between the measurement data and the predicted models, which means a high prediction accuracy was provided by these models. Based on numerical analysis, the R-squared values of all the models fall between 0.4704 and 0.9798, while the RMSE values are in the range of 0.0216 to 2.9008 dB. Moreover, the MAPE values are between 0.37% and 6.94%, and for the correlation factor, the values are in the range of 69.45% to 99.07%. Thus, all the previous metrics results show the quality of the model's predictions for such environments. The reasons behind the accuracy of these models are: (1) The availability of training data since 80% of the measurement data was used to train the models. (2) Efficient input feature selection that considers crucial factors, such as the AoA and the Tx antenna's height for these indoor environments, in addition to essential factors, such as the Tx-Rx separation distance and  
Figure 6: Measured and predicted path loss data for each ML-based model. the multi-frequency operating range of 14 to 22 GHz. (3) The use of the hyperparameter tuning technique, namely Optuna, to choose the best values of the hyperparameters (for example, type of activation function, number of layers, number of neurons, learning rate, number of trees) instead of choosing them manually, which leads to a minimum prediction error. (4) The preprocessing of the data for some models, such as the ANN and RNN-LSTM. As a comparison between the models, the ANN model provides the best average RMSE value, while the worst is for the MLR model. However, the RNN-LSTM, KNN, RF, DT, SVR, and PR show their ability to predict path loss since their average RMSE values were less than 1.1653 dB. This can be validated from the R-squared and the correlation coefficient values that show a minimum of 0.8690 and 0.9342, respectively, close to the ideal value of 1. Table 1 provides the performance metric values of the selected ML-based models. It is worth noting that the table provides three values of each metric, the minimum, average, and maximum value. These values came from the cross-validation technique adopted for this study that divided the measurement data into seven folds to evaluate the stability of each model. It can be seen from the table that the models provide highly stable results since the deviation of each metric from its average to maximum or minimum is small. Furthermore, the results displayed in Table 1 are based on all values of the frequency bands selected for this research (i.e., our ML path loss models are multi-frequency), which means that these models have the ability to accurately predict the propagation loss at the adopted frequency regime. Figure 7 represents the test set prediction error of the ML-based models used in this work. In addition, the figure provides insights into the range of the difference between the measured and ML-predicted path loss models. These curves observe random distributions with average error values around zero with error impulses of 6 dB, as shown in the worst case of the MLR model. This means that the predicted ML-based path loss value has a maximum of only 6 dB difference from the real measured path loss value.