Abstract: A cloud-based system for scheduling and receiving a textual alert message and receiving an intelligence dashboard is provided. The system includes a conversational web GUI that is displayed on the web browser having a text input box and a scrollable panel. The text input box receives a textual alert request having an alert parameter and a threshold number. The conversational web GUI displays on the scrollable panel in sequential vertical positions a textual alert request, textual alert words of the textual alert message, updated textual alert words of the updated textual alert message, and the intelligence dashboard on the scrollable panel that correspond to a conversation between a user and the system.

Abstract: A method for detecting cell positioning anomalies is disclosed. Control plane signaling data packets are collected associated with multiple cells of a communications network. Distance and azimuth values for individual communication sessions are calculated for each cell. A machine learning model is executed using various communication parameters as input to generate a classification for each cell. A list identifying which cells are experiencing anomalies is generated.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: Disclosed herein is a method to determine a geolocation that includes receiving, by a processor, from a base station (BS), radio predictors, a user equipment (UE) location history, and a geolocation of a first UE for which a minimization of drive test (MDT) mode is activated, radio predictors and a UE location history of a second UE for which the MDT mode is not activated, and cell physical parameters. The method includes training, by the processor, a machine learning (ML) model at least based on the radio predictors, the UE location history, and the geolocation of the first UE, and the cell physical parameters. The method includes executing, by the processor, the ML model to determine the azimuth of the second UE and providing, by the processor, to a downstream application, a geolocation of the second UE at least based on the azimuth and the TA of the second UE.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then the geolocation for the UE is predicted using machine learning techniques utilizing a previous generated machine learning model as applied to the radio signal attributes from the incoming UE signaling record.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then the geolocation for the UE is predicted using machine learning techniques utilizing a previous generated machine learning model as applied to the radio signal attributes from the incoming UE signaling record.