# Machine-Learning-Based Path Loss Prediction
## 4 Results and Discussion
The existing machine-learning-based path loss estimation studies are analyzed in general; it is clear from Table 1 that relatively few of them focus on V2V communication scenarios ([23; 31; 34; 35; 41]). However, only [34; 35; 41] studies were conducted for the 5.9 GHz carrier frequency, which is also the focus of our research; in comparison the studies [23] and [31] have focused on different frequencies. These studies presented path loss estimation performances with RF, ANN, and Gaussian Process Regression (GPR) models in various frequency bands in different propagation environments. Refs. [34; 41] presented path loss estimation performances with the RF machine learning model in SU environments with 3.18 MAE and 2.49 RMSE metrics, respectively, while [35] obtained an MAE \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **No Feature Selection** & **PCA** & **MRMR** & **F-Test** \\ \hline RF & 0.0774 & 0.0810 & 0.0821 & 0.08124 \\ GB & 0.0964 & 0.1053 & 0.9874 & 0.9959 \\ ANN & 0.1477 & 0.1488 & 0.1493 & 0.1032 \\ SVR & 0.1533 & 0.1601 & 0.1645 & 0.1585 \\ AB & 0.2198 & 0.1787 & 0.1998 & 0.2156 \\ LR & 0.2780 & 0.2793 & 0.2815 & 0.2832 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of models after feature selection methods (RMSE). of 4.31 in H and U environments with the RF model. In our study, a comprehensive path loss estimation for V2V in an H environment at 5.9 GHz was performed with ANN, RF, LR, GB, SVR, and AB machine learning models and compared with the performances of log-distance, two-ray, and log-ray empirical models. In this context, the LR model with 0.68 R\({}^{2}\), 0.278 RMSE, and 0.2299 MAE has the worst performance among all the machine learning models examined, the RF model with 0.97 R\({}^{2}\), 0.0774 RMSE, and 0.0557 MAE and the GB model with 0.96 R\({}^{2}\), 0.0964 RMSE, and 0.0701 MAE showing the best performances. It is clear that the MAE values obtained for both RF and other models in our study are much lower than the MAE of 3.18 of the RF model in the SU environment, as reported in reference [34], and the MAE of 4.31 in the H and U scenarios, as reported in reference [35]. Furthermore, the RF model reported in our study has a much lower RMSE value (0.0774) compared to the RMSE value of 2.49 in reference [41] for the SU environment. On the other hand, the path loss estimation performance of empirical models such as log-distance, two-ray, and log-ray resulted in RMSE values of 3.9884, 10.038, and 8.4032, respectively, which are considerably higher than those in both the literature and the machine learning models analyzed. In general, these findings clearly emphasize that using machine learning approaches is more effective for path loss estimation. Moreover, our study advances the area by providing a comprehensive parameter set and advanced machine learning models and providing path loss predictions in an H environment. While previous studies generally used datasets consisting of 1000-5000 observations, our study used comprehensive field measurements for H environments and 192,000 observations. In addition, many different algorithms with various features and capabilities (ANN, RF, LR, GB, SVR, and AB) were used, unlike a single algorithm or a few algorithms, as commonly used in other studies, and their performances were compared. Then, the performances of the machine learning models were also compared to traditional empirical models. Moreover, a wider range of environmental and systemic factors was considered with additional factors such as weather, number, and position of antennas, as well as vehicle direction, and an attempt was made to increase the prediction performance. Finally, unlike the single approach frequently seen in other studies, the performance change according to the model and method was investigated using multiple feature selection methods (PCA, MRMR, F-Test) to increase the model performance.