# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 5 Conclusions  
The analysis comprises determining the pathways of signal propagation from the cellular tower location. The signal strength is analyzed at each cell phone user location (receiver's position). The determined propagation routes of the signal follow the shortest route, thus improving accuracy in the determination of signal strength. The signal strength \begin{table}
\begin{tabular}{c c} \hline \hline  & **Improvement in Signal Strength (RGIPT Small Area Total Cell** \\
**6050 Cells of (5 m \(\times\) 5 m) Grid)** \\ \hline  & \% Population gets (range \(-\)50 to \(-\)75 dBm) \\ \hline Main Location & 47.43801653 \\ \hline Location “1” (Tower towards lower population) & 36.36363636 \\ \hline Location “2” (Tower towards higher population) & 59.50413223 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentage change in locations “1” and “2” from cell values of the main location. Figure 14: (**a**) Signal strength map for cell tower location “1”. (**b**) Signal strength map for cell tower location “2”. Figure 15: (**a**) Number of cells between the high and low-populated areas according to the two dB m sets for the three locations of the tower. (**b**) Number of cells for the whole area for the three locations of the tower. determination model is developed based on a 3D rigorous novel point-to-point routing methodology. The signal strength determination model is designed using semi-empirical models of propagation. It is validated with the data of APP-recorded ground-based signal strengths of over 1000 points at the RGIPT and Agra sites. Using the least square technique, the determined model gave an RMSE error of 3.45. The technique performed well and can adapt with 3D LiDAR data, unlike well-known algorithms that are conventionally used for finding the shortest route in 2D. The use of 2D data and constraints of data inputs prevent accurate prediction of signal strength. For example, different parts of the RGIPT campus are predicted to have uniform signal strength, whereas detailed variations in signal strength are modeled for the site. The ground observations corroborated the modeled predictions. Further, the 2D data never allow for any prediction in 3D, e.g., the different floors of a building can have different qualities of signal strength. Therefore, to have a precise determination, high-quality 3D terrain data is required. Thus, the 3D Lidar-based prediction methodology very significantly improved the prediction resolution and accuracy. The approach described above aids in the placement of cell phone towers to ensure improved signal strength at each cell phone user location. The most appropriate height (Z) and position (X, Y) of a cellular tower for the project area of the RGIPT campus is determined to achieve a signal strength between \(-50\) and \(-90\) dB m using the model established above, as shown in Figure S16. Further, users' settlement density can also be considered to customize the cell phone tower location to provide a very high quality between \(-50\) and \(-75\) dB m or better signal to the majority of the campus users. After keeping the population in mind, \(22\%\) of the population who initially got a signal strength between \(-75\) and \(-95\) dB m now receives a signal strength above \(-75\) dB m. The model is applied to determine the cellular tower distribution for the Shahganj area of Agra city, where five towers were previously installed. The five modeled cell phone tower locations indicated, if installed at modeled locations, could have very significantly improved the signal strength quality from \(-50\) to \(-75\) dB m, as shown in Figures S18-S20. Further, the model has indicated that the Shahganj area could have managed the existing quality of signal strength using only four towers instead of five. The model is developed in such a way that it will find out the optimal location for the cell phone tower and evaluate the signal strength at each user location.