## 2 Literature Review
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Paper** & **Propagation Environment** & **Model Training Method** & **Model Interpretation Method** \\ \hline
[11] & Urban propagation environments for 5G & Deep learning & Linear regression (LR) \\  & cellular communication systems & & & SHAP (Shapley additive explanations) and \\  & Millimeter waves for different scenarios, & Extreme gradient boosting (XGB) &
\begin{tabular}{l} SHAP (Shapley additive explanations) and \\ LIME (local interpretable \\ model-agnostic explanations) \\ \end{tabular} \\ \hline
[13] & Rural, urban, suburban, and urban & & & \\  & high-rise environments with different & & \\  & frequency bands and transmitting & XGB + other MLs & SHAP \\  & antenna heights & & \\ \hline
[14] & Indoor environment for 5G & \
\begin{tabular}{l} Artificial neural network (ANN), \\ support vector regression (SVR), \\ random forest (RF), and gradient \\ tree boosting (GTB) \\ \end{tabular} &
\begin{tabular}{l} Permutation feature importance (PFI), \\ Accumulated local effect (ALE) \\ \end{tabular} \\ \hline
[15] &
\begin{tabular}{l} Constructed a dataset with the \\ three-dimensional modeling software \\ Blender and the commercial ray tracing \\ (RT) tool WirelessInSite with a random \\ scattered layout and \\ 6 and 28 GHz frequency bands \\ \end{tabular} & RF & SHAP \\ \hline
[16] &
\begin{tabular}{l} Measurement campaigns at 220 GHz and \\ 280 GHz, where the scenarios include the \\ hallway, lobby, and laboratory \\ \end{tabular} & RF & SHAP \\ \hline
[17] &
\begin{tabular}{l} Large intelligent surface-assisted wireless \\ communication in a smart \\ radio environment \\ \end{tabular} & RF + other MLs & PFI \\ \hline
[18] &
\begin{tabular}{l} Vehicular-to-infrastructure \\ communication systems \\ \end{tabular} & ANN, SVR, RF, and GTB & PFI, ALE \\ \hline
[19] & \
\begin{tabular}{l} UAV-assisted millimeter-wave \\ (mmWave) radio network \\ \end{tabular} &
\begin{tabular}{l} LR, SVR, K nearest neighbors \\ (KNN), RF, XGB, and ANN \\ \end{tabular} & PFI \\ \hline
[20] &
\begin{tabular}{l} Vehicle-to-vehicle (V2V) \\ communication systems \\ \end{tabular} & RF & Mean decrease impurity method \\ \hline
[21] &
\begin{tabular}{l} Low-altitude UAS flights \\ \end{tabular} & RF & Mean decrease impurity method \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of the explainable ML studies of PL prediction. While existing ML methods in XAI-based PL prediction studies are by default opaque models, they often use model-agnostic explanation techniques to unveil the logic behind decisions made by them. Model-agnostic techniques presume ML models to be of a black box nature and try to convey explanations by building surrogate models, either through employing intrinsically interpretable meta models (e.g., by employing linear regression as applied in [11]) or through employing perturbation mechanisms. Perturbation-based mechanisms (applied in [14; 17; 19]) analyze the importance of each input on the model outcome by systematically modifying the input of the model and observing the changes in the output. If the permutation of a specific part of the input considerably alters the model output, then the specified part is considered to be important. Several studies [12; 13; 15; 16] utilize the Shapley value-based explanations (SHAP) concept. SHAP can be considered a perturbation-based concept as well, which computes the average marginal contribution of a feature to the output predicted by the ML model, considering all possible combinations of features [22]. ALE [18] and LIME [12] are further model-agnostic methods used in the literature of PL prediction, which are built based on perturbation and meta-modeling [22]. An ensemble of tree-based models, e.g., GTB, XGB, and RF, can be explained to some degree by means of mean decrease impurity methods [20; 21].