Dynamic estimation of real-time distribution density of wireless devices using machine learning models

A computer system for dynamically generating an estimate of a real-time distribution density of wireless devices in a geographic area, the system comprising:

• at least one hardware processor, and
• at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
• receive real-time network activity data collected at multiple cell sites in a geographic area;
• process the real-time network activity data with a machine learning (ML) model that is trained based on non-real-time network activity data to predict distributions of wireless devices on the geographic area,
• wherein the non-real-time network activity data indicates counts of network activities by wireless devices previously served at different time intervals by the multiple cell sites in the geographic area and includes location data of the wireless devices, and
• wherein the ML model is configured to predict a real-time distribution of wireless devices on the geographic area at different time intervals;
• predict, based on the ML model, a real-time distribution of the multiple wireless devices in the geographic area,
• wherein the real-time distribution is predicted based on a pattern of network activities and locations of wireless devices at prior time intervals that are analogous to a current time interval;
• and
• dynamically generating an estimate of a distribution density of the multiple wireless devices on the geographic area based on the predicted real-time distribution, adjusted by the real-time count of network activities.

A computer-implemented method for training a machine learning (ML) classifier for estimation of a real-time distribution density of wireless devices in a geographic area, the method comprising:

• designating a geographic area into multiple subregions,
• wherein the geographic area is included in a network coverage area of multiple cell sites;
• collecting a training data set including non-real-time network activity data indicating counts of voice, data, or messaging activities by wireless devices previously served at different points in time by the multiple cell sites in the geographic area and includes location data of the wireless devices;
• training an untrained ML classifier with the training data set to associate the non-real-time network activity of the wireless devices at the different points in time with respective subregions of the geographic area based on the location data of the wireless devices thereby obtaining a trained ML classifier that estimates a real-time distribution of the multiple wireless devices across the subregions of the geographic area.