## 5 Discussion  
In Section 4.3, the results for noise prediction were presented, and several important aspects need to be highlighted. In Figure 3 (left), it is noticeable that the models achieved  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **RF** & **DT** & **ET** & **SVR** & **XGB** & **LGBM** & **Transformer** \\ \hline CC & 0.7225 & 0.47 & 0.7321 & 0.0923 & 0.712 & 0.714 & **0.821** \\ \(MSE\) & 110.61 & 245.51 & **107.77** & 228.4979 & 109.51 & 109.36 & 129.805 \\ \(MAE\) & 7.58 & 11.405 & **7.33** & 12.45 & 7.49 & 7.54 & 9.06 \\ \(RMSE\) & 10.51 & 15.66 & **10.38** & 15.1161 & 10.46 & 10.45 & 11.39 \\ \(MAPE\) & 35.13 & 47.78 & 35.01 & 74.74 & 34.36 & 35.52 & **31.25** \\ \(R^{2}\) & 0.5182 & \(-\)0.0069 & 0.5306 & 0.004 & 0.5081 & 0.5083 & **0.573** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Final distance regression comparison metrics of the proposed methods.  
Figure 8: Graphic of the \(MAPE\) (initial distance on the left and final distance on the right) of the proposed methods with \(N_{0}\) between \(-\)114 dBm/Hz and \(-\)174 dBm/Hz.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **RF** & **DT** & **ET** & **SVR** & **XGB** & **LGBM** & **Transformer** \\ \hline CC & 0.7225 & 0.47 & 0.7321 & 0.0923 & 0.712 & 0.714 & **0.821** \\ \(MSE\) & 110.61 & 245.51 & **107.77** & 228.4979 & 109.51 & 109.36 & 129.805 \\ \(MAE\) & 7.58 & 11.405 & **7.33** & 12.45 & 7.49 & 7.54 & 9.06 \\ \(RMSE\) & 10.51 & 15.66 & **10.38** & 15.1161 & 10.46 & 10.45 & 11.39 \\ \(MAPE\) & 35.13 & 47.78 & 35.01 & 74.74 & 34.36 & 35.52 & **31.25** \\ \(R^{2}\) & 0.5182 & \(-\)0.0069 & 0.5306 & 0.004 & 0.5081 & 0.5083 & **0.573** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Initial distance regression comparison metrics of the proposed methods.  
similar performance, except for decision tree and SVR. Interestingly, the best performance occurred at the highest levels of noise, except for SVR, which specialized in a single noise level, \(-144\) dBm/Hz. Figures 3 (right) and 4 (left and right) exhibit similar characteristics to Figure 3 (left). The overall results for random forest, XGB, LGBM, transformer, and extra trees are similar across all the metrics, with random forest and XGB performing particularly well, as demonstrated in Table 6. In contrast, SVR exhibited poor performance overall. Additionally, the correlation coefficients reveal that random forest, XGB, LGBM, and extra trees achieved strong correlations between the predicted and real noise values (Table 6). Due to the limited computational resources and the large amount of generated data, it was not possible to enhance the robustness of the transformer architectures. Furthermore, a search for optimal hyperparameter values for all the models could be a proposed enhancement for the work.  
In Section 4.4, the results for the initial and final distance predictions were presented, and several notable observations arise. In Figure 5, it is evident that extra trees and LGBM achieved the highest performance. Unlike the noise prediction, the best performance for both distance predictions occurred at the lowest levels of noise for all the models, which was expected. The behavior of predicting the initial distance mirrors that of predicting the final distance, as indicated in Figures 5-8. The general results for random forest, XGB, LGBM, transformer, and extra trees exhibit similarity across all the metrics, with extra trees, transformer, and LGBM slightly outperforming in almost all the metrics for both the initial and final distances, as shown in Tables 7 and 8, respectively. In contrast, decision tree and SVR showed poor performance overall. Moreover, the correlation coefficients reveal that the predicted distances exhibited indices greater than 0.82 in relation to the real values in both distances, initial and final. An interesting factor is that, in the initial distance prediction, the \(MAPE\) values for the XGB and LGBM models returned 'inf'. More studies are needed to understand what happened.