## 3 Methods
### Input, Outputs, and Dataset Description  
We analyzed 5 different buildings, two of which are used for training/validating the ML PL model, and 3 for testing. In the buildings used for training/validating the models the information from all APs is in the same dataset to have a large number of PL samples. However, in the buildings used for testing, we use the information from one AP each time. Each building is equipped with several 5G sources (see Table 1), and the PL information is generated according to the WHIPP tool. These PL samples are calculated on a 1 m regular grid spanning the entire building, between APs and receiver locations. The goal of the  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Building** & **Area** & **Diffraction** & **\#5G Sources** & **Total Number** & **Dataset** \\  & **[m\({}^{2}\)]** & **Loss Factor** & **\#5G Sources** & **of PL Samples** & **Type** \\ \hline iGent & \(42\times 27\) & \(1/18\) & \(17\) & \(18\),904 & Test \\ Zuiderpoort & \(82\times 17\) & & \(13\) & \(20\),348 & Training/ \\ Vooruit & \(30\times 37\) & & \(14\) & \(10\),766 & Test \\ Koutitas & \(40\times 29\) & \(1/5.14\) & \(13\) & \(11\),856 & Test \\ Library Jaume I & \(110\times 30\) & & \(23\) & \(72\),266 & Training/ \\ \hline \hline \end{tabular}
\end{table}
Table 1: iGent, Zuiderpoort, and Vooruit are buildings in the city of Ghent, Belgium. The building used in the work by [PERSON] et al. [41], further referred to as ‘Koutitas’. Library Jaume I is the library building of Universitat Jaume I, Castellon, Spain.  
ML algorithm is to estimate the value of these PL (output) samples, not by finding the dominant path, but based on a set of easy-to-calculate inputs:  
* \(log_{10}\) of the distance [m] between the 5G source and the given point [42; 43].
* The total number of walls encountered along the direct line between source and receiver.
* Distance to the wall closest to the 5G source along the direct ray.
* Distance to the wall closest to the receiver location along the direct ray.
* Total cumulated wall loss along the direct ray.
* Average of all wall losses in the building.
* Total wall loss along a ray that rotates the direct ray 10 and 20 degrees clockwise, and counterclockwise from the 5G source, with the same distance as the direct ray. This input is added to try to account for the interaction loss in [3] and Equation (1).  
As the IDP model (see Equation (1)) accounts for the severity of diffraction loss via a diffraction loss factor, which is derived from the main wall types present in the building (heavy, like concrete or brick, or light, like drywall and wood), it is logical to build separate ML models for each building with heavy or light wall types. Table 1 shows these two diffraction loss factors. For both diffraction loss values, a model was be trained, validated, and tested. For buildings with mainly light walls (diffraction loss factor 1/18), we used samples from the Zuiderpoort building to train and validate, and for testing the samples from the iGent building. For buildings with rather heavy walls (diffraction loss factor 1/5.14), we used a dataset with the samples from Library Jaume I for training and validating, and the samples from Vooruit and Koutitas for testing the algorithm.