## 6 Reviewed Papers on Machine-Learning-Based Path Loss Models
Even though only a line-of-sight (LOS) scenario was used in the investigation, the performance of each model was contrasted. The most accurate predictions came from the tree-based ensemble models, with AdaBoost achieving the lowest MAPE value of 2.72%. Table 4 compiles key findings from studies that examined path loss modeling with machine learning techniques. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{ \begin{tabular}{c} **Freq.** \\ **(GHz)/Scenario** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **ML.** \\ **Algorithm** \\ \end{tabular}  } & \multirow{2}{*}{**Input Features**} & \multirow{2}{*}{ \begin{tabular}{c} **Performance** \\ **Indicators** \\ \end{tabular}  } & \multirow{2}{*}{**Important Results**} & \multirow{2}{*}{ \begin{tabular}{c} **Limitations/Area** \\ **of Improvement** \\ \end{tabular}  } \\ \hline \multirow{4}{*}{[74]} & 28/ & \multirow{4}{*}{CNN} & Tx, Rx, floor plan image matrix & RMSE & \begin{tabular}{c} The proposed \\ model \\ outperformed the \\ chosen models, \\ demonstrating a \\ root mean square \\ error of 8.59 dB. \\ \end{tabular}  & \begin{tabular}{c} To accurately \\ determine the \\ model stability, the \\ result should be \\ modeled with \\ multiple \\ parameters. \\ \end{tabular}  \\ \hline \multirow{4}{*}{[75]} & 3.5/ & \multirow{4}{*}{ \begin{tabular}{c} \(D\)-CNN \\ Urban \\ \end{tabular}  } & Satellite image, pl exponent, and shadow factor & MSE & \begin{tabular}{c} The results made \\ public demonstrate \\ a high degree of \\ random element \\ parameter \\ prediction accuracy. \\ \end{tabular}  & \begin{tabular}{c} In commercial \\ environments with \\ more impediments, \\ the path loss model \\ needs to be tested. \\ \end{tabular}  \\ \hline \multirow{4}{*}{[76]} & 28/ & \multirow{4}{*}{RF} & Tx power, Tx antenna gain, terrain profile, and site coordinates & RMSE and cost time & \begin{tabular}{c} This study \\ recommends \\ employing the RF \\ model, as it was \\ proven to be \\ reliable with high \\ accuracy. \\ \end{tabular}  & \begin{tabular}{c} There is a need to \\ compare the \\ proposed model \\ with any of the \\ widely used \\ models in the same \\ environment to test \\ its validity. \\ \end{tabular}  \\ \hline \multirow{4}{*}{[77]} & 3.7/ & \multirow{4}{*}{ \begin{tabular}{c} ANN, \\ RF, SVR, and \\ B-kNN \\ \end{tabular}  } & Distance between Tx and Rx, Tx height, and Rx height & RMSE, ME, MAE, and \(\sigma\) & RMSE on the order of 4.2 to 4.3 dB after a comparison between the proposed ML models and those of the chosen empirical models. \\ \hline \multirow{4}{*}{[78]} & 28/ & \multirow{4}{*}{CNN} & \multirow{4}{*}{LAMS images} & \multirow{4}{*}{RMSE} & The suggested model solved the few-shot data problem and implemented path loss prediction in a smart factory. & There is a need to test the proposed model in a non-line-of-sight scenario to deduce its stability in the indoor environment. \\ \hline \multirow{4}{*}{[79]} & \multirow{4}{*}{2.5/Suburban, Urban} & \multirow{4}{*}{ANN} & \multirow{4}{*}{\ \begin{tabular}{c} These PL \\ prediction models \\ become more \\ accurate and stable \\ transmitted and \\ receiver power, \\ antenna \\ information, and \\ feeder loss \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} The suggested \\ model’s viability \\ needs to be \\ experimental \\ experimental \\ \end{tabular}  } \\ \cline{1-1}  & & & & & \\  
A deep learning model, such as the LSTM, was used to develop a way of predicting fluctuations in path loss [82]. The training and validation data were taken from measurements of path loss in an urban environment. The model was compared to a conventional approach that predicts using the most recent observed median path loss value, utilizing 100 fast-fading data points as input data. In the validation analysis, the measurement campaign was restricted to an urban environment, and the error analysis was limited to the root mean square error.