# Machine-Learning-Based Path Loss Prediction
## 5 Conclusions  
This study shows that integrating machine learning algorithms for path loss estimation in V2V communication offers better performance than traditional empirical models. In particular, when large datasets and environmental factors are combined, machine learning algorithms can estimate the results more accurately in complex and dynamic environments. For this purpose, a comprehensive dataset was created with experimental measurements conducted in highway environments in the Trabzon and Gumushane provinces at 5.9 GHz, 85% of which was used for training and 15% for testing. Path loss estimations were made using ANN, RF, LR, GB, SVR, and AB machine learning models, and the performances of these models were compared with the empirical log-distance, two-ray, and log-ray models used in highway environments. Here, the RF model obtained the lowest RMSE of 0.0774 and an MAE of 0.0557, whereas the GB model showed the second-best performance, with an RMSE of 0.0964 and an MAE of 0.0701. LR was the worst-performing model among the machine learning algorithms, with an RMSE of 0.278 and an MAE of 0.2299. Traditional experimental models exhibited significantly higher RMSE values (\(\geq\)3.9884 ) than all the machine learning models used. In addition, feature selection methods such as PCA, MRMR, and F-Test significantly improved the model performance, especially in AB and ANN, although their effects varied for different algorithms. The RMSE value (0.1032) obtained for the ANN model using the F-Test showed the third-closest performance to the RMSE values of the RF and GB models. This also suggests the need for a model-specificinvestigation of feature selection and weighting. This study contributes to the existing literature by evaluating the performance of different machine learning techniques and provides important practical results for the design and optimization of V2V communication systems. The performance comparisons emphasized the strengths and weaknesses of machine learning approaches compared to experimental models, and ultimately, a more robust solution was proposed for path loss estimation. In general, our findings emphasize the necessity and effectiveness of using advanced machine learning techniques for accurate and reliable path loss estimations in V2V communications. Therefore, by using accurate path loss predictions to optimize the signal density in V2V communication, the reliability of data transmission may be enhanced, leading to the development of safe driving, particularly for autonomous vehicles. Moreover, these calculations can improve the efficiency of the traffic flow and reduce the probability of accidents by minimizing communication interruptions in highly congested locations.  
Conceptualization, N.S. and Z.H.T.; methodology, N.S. and Z.H.T.; software, N.S.; validation, Z.H.T.; formal analysis, Z.H.T.; investigation, N.S. and Z.H.T.; resources, N.S. and Z.H.T.; data curation, Z.H.T.; writing--original draft preparation, N.S. and Z.H.T.; writing--review and editing, Z.H.T.; visualization, N.S.; supervision, Z.H.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.