## 5 Conclusions  
We proposed a novel indoor network planning approach based on the combination of machine learning and a genetic algorithm. We used the Decision Tree Ensembles (Bagging) algorithm to build a generic model able to estimate the PL. The model was trained/validated with easy-to-calculate information from a building (e.g., number of walls between the AP and the point, total wall loss along the direct ray) and validated in three other buildings, yielding an MAE lower than 3 dB, compared to an advanced dominant path loss model accounting for physical characteristics of the environment. Subsequently, this ML model was used in a GA to perform network planning for finding a deployment with a minimal number of AP, and still delivering the required throughput. The results were compared to a heuristic optimizer in terms of the number of required APs to provide coverage, the time to calculate this deployment, and the deployment accuracy. The proposed ML + GA-based solution achieves the same, or better, results in the number of required AP to satisfy different reception conditions, is 15 times faster, and provides full flexibility towards replanning, with only minor impact on model performance.  
Future work will consist of a time optimization for the features (inputs) calculation process, where the PL for all possible AP-grid point links is calculated to be used in the GA. A modification to the cost function in the GA will be added to target minimal human exposure, and a higher SIR. Finally, this approach will be tested for accurate wireless network planning using ray-tracing path loss models.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Possible APs**} & \multicolumn{2}{c}{**WHIPP Tool**} & \multicolumn{2}{c}{**ML + GA**} & \multirow{2}{*}{} \\ \cline{3-3} \cline{5-6}  & & **Required APs** & & **Time [min]** & & **Required APs** & **GA Calculation Time [min]** & **\% Rx Covered** \\ \hline iGent & 52 & 7 & 1.82 & 7 & 0.14 & 100 \\ Züüüdergoort & 58 & 9 & 27.22 & 9 & 0.22 & 100 \\ Vooruit & 50 & 4 & 1.05 & 4 & 0.08 & 100 \\ Koutitas & 45 & 16 & 18.13 & 12 & 0.13 & 100 \\ Library Jaume I & 187 & 35 & 1709.00 & 31 & 2.58 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between the WHIPP and ML + GA network planners, with respect to the required number of APs, the time to calculate the network deployment, and coverage percentage for the setting 15/256 QAM/30 dB/1000 Mbps/-53 dBm from Table 2.  
**Author Contributions:** Conceptualization, Y.H.S., D.P. and W.J.; methodology, D.P. and R.M.A.; software, Y.H.S.; validation, Y.H.S. and D.P.; formal analysis, Y.H.S.; resources, R.M.A. and G.G.N.; writing--original draft preparation, Y.H.S.; writing--review and editing, D.P., R.M.A., G.G.N. and W.J.; visualization, Y.H.S.; supervision, W.J., D.P. and G.G.N.; project administration, W.J., D.P. and L.M.; funding acquisition, W.J. and L.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Special Research Fund (BOF-Dutch abbreviations), Belgium, grant number 01W00818, and _LACETEL_, Cuba.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.