US 11,146,978 B2Granted

KPI anomaly detection for radio access networks

AssigneeT-Mobile USA, Inc.

InventorsPrem Kumar Bodiga, Norlinda Langub, Adam Lemow, Hermie Padua, John Carlo Ventura, Ariz Jacinto

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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.

Claims15

Dependent claims

A computing device operating as a network node in a core network of a cellular network, the computing device being configured to monitor a radio access network (RAN) of the cellular network, the RAN comprising a plurality of clusters, each cluster comprising a plurality of sites and a plurality of cells, each site communicates over one or more frequency or time divisions with cellular devices that are located in one or more cells, the computing device comprising:

one or more processors;
and
a computer-readable medium storing a plurality of computer-executable components that are executable by the one or more processors to perform a plurality of actions, the plurality of actions comprising:
receiving a plurality of key performance indicator (KPI) measurements from the plurality of clusters, each of the plurality of KPI measurements generated for one of a plurality of KPI types;
receiving information identifying anomalous KPI measurements in the received KPI measurements;
for a cluster of the RAN, identifying one or more common anomalous KPI types that satisfy a ubiquity criterion, wherein a particular KPI type satisfies the ubiquity criterion when more than a threshold percentage of the sites in the cluster have anomalous KPI measurements generated for the particular KPI type;
determining, for each of the identified common anomalous KPI types for the cluster, a classification from a group of classifications, wherein at least one classification corresponds to two or more of the identified common anomalous KPI types;
ranking 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;
outputting a list of sites of the cluster that are ranked based on anomaly metrics that are derived from the anomalous KPI measurements for the sites of the cluster;
receiving a selection of a site from the list of sites;
outputting a list of KPI types for the selected site that is ranked based on anomaly metrics that are derived from the anomalous KPI measurements of the plurality of KPI types for the site;
and
outputting a list of common anomalous KPI types and corresponding classifications for the cluster based on the ranking.

10 more elements

A computer-implemented method for monitoring a radio access network (RAN) comprising a plurality of clusters, each cluster comprising a plurality of sites and a plurality of cells, each site communicating over one or more frequency or time divisions with cellular devices that are located in one or more cells, the method comprising:

receiving, at a network node of a core network associated with the RAN, a plurality of key performance indicator (KPI) measurements from the plurality of clusters, each of the plurality of KPI measurements generated for one of a plurality of KPI types;
receiving information identifying anomalous KPI measurements in the received KPI measurements;
for a cluster of the RAN, identifying one or more common anomalous KPI types that satisfy a ubiquity criterion, wherein a particular KPI type satisfies the ubiquity criterion when more than a threshold percentage of the sites in the cluster have anomalous KPI measurements generated for the particular KPI type;
determining, for each of the identified common anomalous KPI types for the cluster, a classification from a group of classifications, wherein at least one classification corresponds to two or more of the identified common anomalous KPI types;
ranking 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;
outputting a list of sites of the cluster that are ranked based on anomaly metrics that are derived from the anomalous KPI measurements for the sites of the cluster;
receiving a selection of a site from the list of sites;
outputting a list of KPI types for the selected site that is ranked based on anomaly metrics that are derived from the anomalous KPI measurements of the plurality of KPI types for the site;
and
outputting a list of common anomalous KPI types and corresponding classifications for the cluster based on the ranking.

7 more elements

One or more non-transitory computer storage media of a computing device storing computer-executable instructions for monitoring a radio access network (RAN) comprising a plurality of clusters, each cluster comprising a plurality of sites and a plurality of cells, each site communicating over one or more frequency or time divisions with cellular devices that are located in one or more cells, wherein upon execution the computer-executable instructions causing one or more processors to perform acts comprising:

receiving, at a network node of a core network associated with the RAN, a plurality of key performance indicator (KPI) measurements from the plurality of clusters, each of the plurality of KPI measurements generated for one of a plurality of KPI types;
receiving information identifying anomalous KPI measurements in the received KPI measurements;
for a cluster of the RAN, identifying one or more common anomalous KPI types that satisfy a ubiquity criterion, wherein a particular KPI type satisfies the ubiquity criterion when more than a threshold percentage of the sites in the cluster have anomalous KPI measurements generated for the particular KPI type;
determining, for each of the identified common anomalous KPI types for the cluster, a classification from a group of classifications, wherein at least one classification corresponds to two or more of the identified common anomalous KPI types;
ranking 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;
outputting a list of sites of the cluster that are ranked based on anomaly metrics that are derived from the anomalous KPI measurements for the sites of the cluster;
receiving a selection of a site from the list of sites;
outputting a list of KPI types for the selected site that is ranked based on anomaly metrics that are derived from the anomalous KPI measurements of the plurality of KPI types for the site;
and
outputting a list of common anomalous KPI types and corresponding classifications for the cluster based on the ranking.

7 more elements

Dependencies are inferred from the claim text.