# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 5 Results and Discussions
However, the runtime varies according to the performance of the computer used. Figure 7: Prediction error of each ML-based model. The experimental platform is on a PC with an Intel Core i7 processor, Gen (10) 1.20 GHz cores, 64-bit operating system, and an x64-based processor. It also has 1 TB shared memory and 16.0 GB RAM. The software used for the model implementation includes Python Version 3.5.2, Tensor flow backend 1.1.0, and TFlearn 0.3. The adopted algorithms have been used from two build-in python libraries called Scikit-learn (Sklearn) and Tensorflow (TF). These are the most useful and robust libraries for machine learning in Python. It provides efficient tools for machine learning modeling, such as classification, regression, and clustering. Training and testing times for the results are provided in Table 2 for each model. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **RunTime (seconds)** \\ \hline MLR & 0.0213 \\ PR & 0.5251 \\ SVR & 3.8647 \\ DT & 0.1379 \\ RF & 11.2477 \\ KNN & 0.0782 \\ ANN & 59.1485 \\ RNN-LSTM & 124.9184 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Runtime comparison of the adopted ML models. Figure 8: Measured vs. predicted path loss data for each model. To investigate the impact of choosing the antenna height as an input feature, we removed the Tx antenna height column from the datasets' input features. Table 3 provides the performance metrics' values when the adopted ML-based models have three input features: the Tx-Rx separation distance, operating frequency, and the AoA. It is clear from the results that the overall performance of the models became worse than when the models had all four input features. For example, the average R-squared and the correlation coefficient values are reduced by approximately 3% and 4%, respectively, while the average RMSE value was increased by 1.6942 dB. For the RNN-LSTM model, the average RMSE was also increased by 1.3731 dB. The results presented in Table 3 indicate the importance of considering the Tx antenna as an input feature. Furthermore, the impact of only having the separation distance and frequency as the two input features (removing the Tx height and the AoA) is investigated. Table 4 shows the values of the performance metrics after removing both the Tx height and the AoA from the input features. Again, the accuracy of the models became worse compared to the results provided in Tables 1 and 3. For instance, the average R-squared value and the correlation coefficient reductions are approximately 9.3% and 5.2%, respectively. In addition, the average RMSE value was increased by 1.9673 dB for the ANN model, while for the RNN-LSTM model, the matter was increased by 1.9808 dB. Again, the results reveal the effectiveness of having the four input features together. Based on the results provided, it is clear that the careful selection of the input features for training the models can ensure high prediction accuracy without considering more features, which leads to less complexity in using the proposed models for such environments. However, there is a high demand for developing precise mechanisms capable of generalizing the results for data far different (representing other communication environments) from the data used for training the models, to overcome the need to conduct extensive measurement campaigns in all the possible communication scenarios. Furthermore, these generalizing mechanisms will allow for adaptive data-driven prediction models that accurately represent the channel characteristics for future mobile networks relying on fewer training samples, which leads to faster and more cost-effective planning of the wireless systems. As a suggestion for expanding the datasets, our improved CI and FI models [10] can be used in this manner since they provide higher precision than the standard models in terms of fitting the real measurement data for path loss prediction.