Abstract: A network performance degradation detection system is provided. The system receives Key Performance Indicator (KPI) values for devices of different device types running different software versions. The system determines baseline values of a first device type by averaging the KPI values of the different software versions running on devices of the first device type. The system compares KPI values for a first software version running on devices of the first device type with the determined baseline values to produce a set of comparison results. The system applies the set of comparison results to a classification model to determine whether the first software version running on devices of the first device type causes network performance degradation.

Abstract: The disclosed system identifies international calling performance issues of a wireless telecommunication network. The system receives network traffic data for international calls including information about call attempts to a country. The system categorizes the country into a major category and a minor category based on the call attempts information. For a subset of countries, and for each key performance indicator in a subset of selected key performance indicators, the system monitors performance using an anomaly detection model to identify an anomaly in network performance, determines an actual value of the key performance indicator for the detected anomaly, and computes a variation value of the determined actual value based on a predicted range of values. The system ranks countries using the computed variation values, to indicate problematic parts of the wireless telecommunication network.

Abstract: The disclosed system identifies international calling performance issues of a wireless telecommunication network. The system receives network traffic data for international calls including information about call attempts to a country. The system categorizes the country into a major category and a minor category based on the call attempts information. For a subset of countries, and for each key performance indicator in a subset of selected key performance indicators, the system monitors performance using an anomaly detection model to identify an anomaly in network performance, determines an actual value of the key performance indicator for the detected anomaly, and computes a variation value of the determined actual value based on a predicted range of values. The system ranks countries using the computed variation values, to indicate problematic parts of the wireless telecommunication network.

Abstract: The disclosed system identifies international calling performance issues of a wireless telecommunication network. The system receives network traffic data for international calls including information about call attempts to a country. The system categorizes the country into a major category and a minor category based on the call attempts information. For a subset of countries, and for each key performance indicator in a subset of selected key performance indicators, the system monitors performance using an anomaly detection model to identify an anomaly in network performance, determines an actual value of the key performance indicator for the detected anomaly, and computes a variation value of the determined actual value based on a predicted range of values. The system ranks countries using the computed variation values, to indicate problematic parts of the wireless telecommunication network.

Abstract: The disclosed system identifies international calling performance issues of a wireless telecommunication network. The system receives network traffic data for international calls including information about call attempts to a country. The system categorizes the country into a major category and a minor category based on the call attempts information. For a subset of countries, and for each key performance indicator in a subset of selected key performance indicators, the system monitors performance using an anomaly detection model to identify an anomaly in network performance, determines an actual value of the key performance indicator for the detected anomaly, and computes a variation value of the determined actual value based on a predicted range of values. The system ranks countries using the computed variation values, to indicate problematic parts of the wireless telecommunication network.

Abstract: A network performance degradation detection system is provided. The system receives Key Performance Indicator (KPI) values for devices of different device types running different software versions. The system determines baseline values of a first device type by averaging the KPI values of the different software versions running on devices of the first device type. The system compares KPI values for a first software version running on devices of the first device type with the determined baseline values to produce a set of comparison results. The system applies the set of comparison results to a classification model to determine whether the first software version running on devices of the first device type causes network performance degradation.

Abstract: An analyzer configured to monitor a radio access network (RAN) of a cellular network is provided. The RAN includes multiple clusters that each includes multiple sites and multiple cells. The analyzer receives a multiple key performance indicator (KPI) measurements from the multiple clusters. Each KPI measurements generated for one of several KPI types. The analyzer receives information identifying anomalous KPI measurements in the received KPI measurements. For a cluster of the RAN, the analyzer identifies one or more common anomalous KPI types that satisfy a ubiquity criterion. The analyzer ranks the identified common anomalous KPI types for the cluster based on an anomaly metric that is derived from the anomalous KPI measurements generated for each identified common anomalous KPI type. The analyzer outputs a list of common anomalous KPI types for the cluster based on the ranking.

Abstract: An analyzer configured to monitor a radio access network (RAN) of a cellular network is provided. The RAN includes multiple clusters that each includes multiple sites and multiple cells. The analyzer receives a multiple key performance indicator (KPI) measurements from the multiple clusters. Each KPI measurements generated for one of several KPI types. The analyzer receives information identifying anomalous KPI measurements in the received KPI measurements. For a cluster of the RAN, the analyzer identifies one or more common anomalous KPI types that satisfy a ubiquity criterion. The analyzer ranks the identified common anomalous KPI types for the cluster based on an anomaly metric that is derived from the anomalous KPI measurements generated for each identified common anomalous KPI type. The analyzer outputs a list of common anomalous KPI types for the cluster based on the ranking.