# Machine-Learning-Based Path Loss Prediction
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Freq. (MHz)** & **Environment** & **Input Parameters** & **Output Parameter** & **Test Model** & **RMSE (dB)** & **Study Area** \\ \hline
[10] & 930 & Railway & Distance between Base station & Path Loss & ANN & 1.10 & Other \\  & & & and Receiver & Path Loss & ANN & 1.10 & Other \\ \hline
[11] & 900 & Suburban & Base Station transmission & & & & \\  & & power, Mobile Station receiver & Path Loss & ANN, SVR, MLR & ANN = 0.00493 MLR = 0.051 & Other \\  & & & power, Frequency, Distance & & & \\ \hline  & & Antenna horizontal deflection & & & & \\  & & High-building city, theta, effective height of & & & & \\  & & and quasi-flat terrain & & & & \\  & & & & & \\  & & & & & \\ \hline
[13] & 1800 & Highway(Nigeria) & Normalized distance between the base station and the mobile station. & Path Loss & ELM, Okumura-Hata & ELM = 4250 & Okumura-Hata = 8.73 & Other \\ \hline
[14] & 900 & Urban environments (Greece) & Coordinates of the Transmitter and Receiver, Distance along the \(y\)-plane, Features depicting the intersections between the buildings and the LOS path & Path Loss & ANN SVR RNN RF AB & ANN : 2.95 SVK: 302 kNN: 2.97 & ANN : 2.85 XGB: 2.84 AB: 2.92 & Other \\ \hline  & & & Distance between transmitter and receiver, Street width, & & & \\
[15] & 1890 & Urban and Suburban Environments (Greece) & Building separation, Building height, Difference between base station antenna height and radio height, & Path Loss & RBF-NN & 3.71 & Other \\ \hline
[16] & 1800 & Smart campus (Nigeria) & Longitude, Littletude, Elevation, Altitude, Clutter height, Distance & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of machine-learning-based path loss estimation studies in the literature. \begin{table}
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[18] & 203.25 & Urban (Nigeria) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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[19] & 1800 & Suburban (Brazil) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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[20] & 877.26, 2021.42, 2127.6 & City (Beljing, China) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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[21] & 3700, 2600 & City (Lishon, Portugal) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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[22] & 2140 & City (Frankfurt, Germany) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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[23] & 450, 1450, 2300 & Suburban (Korea) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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[24] & 3700 & Rural (Greece) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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[25] & 5900 & City (Berlin, Germany) & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \begin{tabular}{} \end{tabular} & \
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\end{table} 
Table 1: Cont.