# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 2 Research Question on the Placement of Cellular Tower(s)  
Increased usage of cell phones and the need for higher bandwidth necessitates placing cellular towers in optimal locations. However, there are several issues associated with placing a cellular tower in a particular place. In the GIS environment, primarily the spatial  
Figure 1: Reduction of weak area and interference (Reprinted from Ref. [21]). Figure 2: (**a**) Signal propagation, (**b**) Signal strength range. and non-spatial attributes are considered to determine the optimal solution for finding out the cell tower location(s). The distance of towers from cities, highways, other towers is weighed considering their geographic location along with non-spatial attributes of population, income, etc., for the determination of optimal location. The determination of all these distances requires the creation of shapefiles for cities, highways, populations, etc. Shapefiles are also required to be in the same coordinate system to carry out any spatial analysis, e.g., the position of a cellular tower near the city, highway, city, highway. Spatial analysis in GIS is often carried out through the generation of buffer areas around each shapefile based on pre-decided distance and comparing them by overlay analysis. Thus, the buffer areas around cities, highways, and existing towers are required to be determined. The buffer raster analysis indicates the locations where there is no coverage and overlapping areas. Further various weighting criteria are used for the distance to the city, highway, or other towers to decide an optimum location for setting up a new tower. It is challenging to find the optimal location of setting up a cellular tower for an area having land cover objects of different sizes, shapes, and volumes and having land uses of different types. Similarly, finding the best locations for setting up multiple cellular towers to cover a city is difficult. In computer science, it can lead to solving an NP-Hard problem. GIS-based models for cellular distribution do not consider the cases of the presence of multiple towers in an area of different service providers. It can so happen that the quality of signal in a place may be better from a service provider compared to another. The model should be able to distinguish it and suggest a better service provider for a place. GIS-based modeling for cellular towers demands significant data processing. It is required to produce the shapefiles for city, highway, tower, population, income rate, detailed land cover, and land uses. Further, these shapefiles are required to be compared with the location of the tower(s) to determine the spatial objective function giving appropriate weights in line with importance. Different models suggested by researchers are either theoretical solutions or GIS-based solutions indicating sufficiency of distribution or dead zones. Primarily, the research does not verify their prediction with the ground data. Thus, the efficiencies of these solutions are doubled. The existing optimum cellular tower locating algorithms are primarily theoretical or are using very simplified rough 2D terrain data of land-use and landcover to ascertain the best location for setting up a cellular tower using simplified objective functions. In the absence of a detailed and practical approach to adapt to any urban setup precisely, the GIS approaches follow average techniques to determine the optimality. It invariably results in over or under design. There is, thus, a need to evolve a technique that should be able to use the detailed variation in topography for an area and estimate the detailed obstructions or impacts of each for the signals to reach any user location from a cell phone tower. It is required to be understood that the urban environment, having adjacent high-rise buildings located between the cellular tower and user location, plays a very big role in terms of the obstruction of signals.