# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 3 Methodology  
The determination of an accurate model for the estimation of the optimum location and number of cellular towers involved the estimation of a technique consisting of the following components:  
1. Establishment of an accurate model to predict the signal strength at a user location after transmission of a signal from a cellular tower location. 1. Development of a model inputting accurate 3D terrain data, cellular tower and user location(s), and signal strength at tower location to determine the strength at desired user locations. 2. Extraction of detailed routes for transmission of signals using 3D LiDAR data. 3. Estimation of terrain parameters and dependent transmission losses for every route between each tower and user pair as a modeling input. 4. Verification of the model for a wide variety of terrain geometry and a large number of points. 5. Generation of 3D signal strength map for a cellular tower to its all-neighboring users' locations. 2. Establishment of a technique to determine the optimum location for setting up a cellular tower using an accurate signal strength prediction model. 1. Estimation of optimum location in X, Y, and Z for an area. 2. Determination of optimality at the rough grid in X, Y, e.g., 100 m \(\times\) 100 m, then refinement to a very fine grid, 2 m \(\times\) 2 m. 3. Determination of optimality in height (Z) about heights of user's locations. 3. Establishment of a technique to determine the optimum number and locations of the cellular tower for an area to offer good/acceptable quality of signal strength to most users. 1. Estimation of number and locations of the cellular tower using a signal strength prediction model and optimality prediction model. 2. Establishment of global optimality maintaining a trade-off between over design and under design and good/adequate signal strength requirement for an area. The need for establishing a technique to determine the optimum location(s) for the setting up of cellular towers accurately was understood. The technique would depend on the use of very accurate 3D terrain data, which in the present study was used with LiDAR data. It is required to determine all the routes through which the signal can transmit from a cellular tower location to a user location. The routes can be direct where there is no obstruction between the tower and user location; it can also be indirect where there is/are obstruction(s) between the two. The route determination algorithm required the extraction of buildings, roads, open spaces, etc., between the cellular tower and the users' locations. Once routes can be determined, these are used to determine terrain parameters (e.g., path difference, path length, reflecting ground) for the computation of signal losses. The information of locations of the tower (or the potential location of the tower) and a user pair, topographical obstructions between them, and the strength of signal of the cellular tower can be integrated using a model to predict the likely signal strength for the user location. The model derived can be used for predictions in _one-to-one_ or _one-to-many_ modes. The prediction of signal strength whenever applied for one cellular tower and one location for user comprised the _one-to-one_ mode, while when there were many users to one cellular tower location, it constituted the _one-to-many_ prediction mode. Essentially _one-to-many_ is used for making a map of predicted signal strength for a tower location. The model integrating signal strength of tower, locations for tower and users, and the transmission losses (dependent on terrain parameters) are designed in line with the transmission mechanisms of radio waves (analogous with the transmission of the sound wave). The signal strength prediction model's semi-empirical equations were established with the least square technique. The modeling equations took ground observations with a signal strength of 500 points around an existing location of the tower to comprehensively validate the model.