# Machine-Learning-Based Path Loss Prediction
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Ref.** & **Freq. (MHz)** & **Environment** & **Input Parameters** & **Output Parameter** & **Test Model** & **RMSE (dB)** & **Study Area** \\ \hline
[33] & 1890 & Urban (Greece) & Distance, Street width, & & & & & \\  & & & Building height, Distance & & & & & \\  & & & between building blocks, Tx & Path Loss & ANN & 5.8-6.6 & Other \\  & & & & antenna position with & & & & \\  & & & respect to roof height & & & & & \\ \hline
[34] & 5900 & Suburban & Distance, Type of obstacle & Path Loss & RF & MAE\(\lambda\)18 & V2V \\  & & & between Tx and Rx & & & & \\ \hline
[35] & 5860 & Urban and Highway & Distance, Direction, Relative & & & & & \\  & & Scenarios & Speed Between Two Vehicles & Path Loss & RF & MAE\(\lambda\)31 & V2V \\ \hline
[36] & 3700 & Suburban (Korea) & Distance, LoS/NLoS status, & & & & & \\  & & & Effective height between Tx & Path Loss & ANN, RF, SVR, ANN & RF \(\pm\) 4, SVR \(\pm\) 4, ANN \(\pm\) 4, ANN \(\pm\) 42 & Other \\  & & & and Rx & & & & \\ \hline
[37] & 3520 & Aircraft Interior Cabin & Location data, Antenna & & & & \\  & & & configurations, Channel & Path Loss & SVR & 0.03-143 & Other \\  & & & impulse response matrix & & & & \\ \hline  & & & Tx-Rx distance, Frequency, & & & & \\  & & & Number of walls and floors & & & & \\  & & & crossed, Collision angle with & & & & \\  & & & walls and floors, furniture & & & & \\  & & index and human & & & & \\  & & density ratio & & & & & \\ \hline
[39] & 1800 & Urban, rural, suburban & & & & & \\  & & & & Altitude, Obstacle height, & & & \\  & & & Elevation & & & & \\ \hline
[40] & 3000-6000 & Urban & Distance, Frequency & Path Loss & ANN & 6.10 & Other \\ \hline
[41] & 5900 & Suburban & Distance, Tx and Rx & & & & \\  & & & Little-Logitude, Info, & & & \\  & & & Integer variable that & & & & \\  & & & distinguishes vehicles & & & \\  & & & between Tx and Rx & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Many of these studies focused on various environments, such as urban (U), suburban (SU), rural (R), highway (H), and specific areas, such as campuses or indoor environments, and only a small number specifically addressed V2V scenarios ([23; 31; 34; 35; 41]). Among these studies, [31] focuses on the Visible Light Communication (VLC) standard in campus road and indoor settings. While [23] provides a path loss estimation in the parking garage environment, it also offers a path loss estimation for SU environments at different frequencies (450, 1450, and 2300 MHz), which differs from the 5.9 GHz DSRC (Dedicated Short-Range Communication) standard we are focusing on in this study. On the other hand, researchers [34; 41] who investigated the path loss in V2V environments for the 5.9 GHz DSRC standard based on IEEE 802.11p estimated the path loss in SU environments, while [35] used the Random Forest technique to estimate the path loss in both H and U environments. Unlike many existing studies, we aimed to examine path loss estimation in V2V communications more comprehensively under highway conditions. This study is necessary because accurate path loss estimation is critical for the development and optimization of V2V communication systems, particularly in dynamic and high-speed environments such as highways. Examining the existing studies, we found that only one study [35] concentrated on H scenarios in V2V, revealing a model performance specific to the RF technique. Therefore, we investigated the path loss estimation performances of various techniques, including Artificial Neural Networks (ANNs), Random Forest (RF), Linear Regression (LR), Gradient Boosting (GB), Support Vector Regression (SVR), and AdaBoost (AB) in our study. Additionally, we compared the model performances of traditional empirical path loss models in H environments, such as log-distance, two-ray, and log-ray, with those of machine learning techniques.