Abstract: Present disclosure generally relates to location tracking and communication systems. More particularly, the present disclosure relates to systems and methods for predicting user location from radio data of telecommunication network. The system may utilize Global Positioning System (GPS) data along with Radio Frequency (RF) data obtained from interactions between the first computing device and network elements such as cells. The RF data along with the GPS data may be used for learned distance prediction models in AI engine. When the first computing device may be latched to a cell, the system via the first computing device can observe one or more neighbour cells. To estimate location of the user, predicted distances from cells and grids that can be served from each of cells may be used by system to estimate location of the user. System may use predicted distance from different cells to estimate the location of the user.

Abstract: The present invention provides a method and system for predicting tropospheric interference using a digital twin model for detecting tropospheric interference and suggesting actions for mitigating the effects of the tropospheric interference. The system (110) may include a machine learning engine (216) having a digital twin model to predict/score the likelihood of cell pairs having the tropospheric interference at a given point of time using cell configuration data, weather data, Hepburn data at that point of time by identifying cell pairs whose likelihood of interference is high. The system (110) is further configured to explore the effect of changing cell configuration parameters like remote electrical tilt and identify actions that can mitigate tropospheric interference with minimal impact on coverage.