T-Mobile Innovations LLC

T-Mobile USA, Inc.

Vikas Ranjan

Aditi Saluja

Raymond Wu

Ting Zhang

Antoine T Tran

Emile Minh Tran

Wei Huang

Lance Paul Lukens

Julio C Costa

Anandajothi Muttayane

Syed Umair Ahmed

George Cristian Apatachioae

Anselmo Myungsup Shim

Michael Valentino

Bhavin Mistry

Sridhar Bethamsetty

A method of initiating a KPI backfill operation for a cellular network based on detecting a network data anomaly, where the method includes receiving, by an anomaly detection and backfill engine (ADBE) executed by a computing device, a data quality metric that is based on a KPI of the cellular network; detecting, by the ADBE, the network data anomaly based on the data quality metric being more than a threshold amount different than a predicted value for the data quality metric, where the network data anomaly indicates that at least a portion of a data stream from which the KPI is calculated was unavailable for a previous iteration of the KPI; and providing, by the ADBE and based on detecting the network data anomaly, a backfill command to a backfill processing pipeline to perform the backfill operation by reaggregating the KPI when the portion of the data stream becomes available.

Automated data backfilling for network data processing

System and methods for selecting customers to notify of a service interruption or outage are described. A network may receive an interruption notification indicating when a service interruption is to occur. In some examples, the interruption notification may also include a particular set of equipment, such as a base station, that will experience the interruption, such as being taken offline for maintenance or a power outage affecting the base station. A notification module determines which customers will be affected by the service interruption and transmits a notification to the customers, informing the customers of the service interruption.

User notification for service interruption

A machine learning system automatically diagnoses and resolves issues in a telecommunications network. When a customer reports a network issue using a mobile application, the device performs a diagnostic test, such as a speed test. In addition, network logs or performance metrics during occurrence of the network issue are collected. The results of the diagnostic are used as inputs to a machine learning model in combination with the network logs or metrics to predict the cause of the network issue or to perform a corrective action.

Machine learning system for predicting network abnormalities

In various examples, upon receiving data comprising a plurality of key performance indicators (KPIs), an anomaly may be detected in a first KPI. An engine may apply rules associating two or more KPIs together to confirm, reject, or narrow the anomaly in the first KPI. Through this technology, the mean time for detecting an anomaly in a network may be reduced thereby preventing the potential for more serious anomalies in the network.

Network anomaly detection using a multivariate-relations engine

A system including a processor configured together and analyzing network activity data associated with a plurality of network service providers, gather geographic reference data that includes information about locale(s) of interest, create bins and use spatial matching to associate network performance parameters with hex bins associated with the locale(s) of interest. Generate, based at least in part on the geographic network data, a geospatial map overlay for incorporation in an interactive graphical user interface (GUI), the geospatial map overlay comprising a visual indication of one or more areas of interest and associated aggregated network performance data, and identify and deploy one or more measures to improve network performance and thus customer experiences for at least one or more of the identified locale associated with a bin.

Network analytics engine incorporating geospatial mapping and area of interest information and associated methods

A method for monitoring and rebooting a node of a wireless communications network is disclosed. The method includes acquiring an incident indication for a node site indicative of a performance drop. The method further includes analyzing the incident indication to identify at least one key term. The method further includes accessing a set of prior incidents having a prior incident indication including the key term. The method further includes providing a recommendation to a user device based at least in part on the prior incident indication that includes a site reset for the node site. The method further includes acquiring a resolution indication following the site reset. The method further includes logging the resolution indication as a prior incident indication in the set of prior incidents.

Base station node monitoring and rebooting

A method for a base station in communication with a user device through a wireless communications network is described. The method includes communicating with a user device on a current frequency through a wireless communications network. The method may also include determining, based at least in part on a model, a probability that a higher capacity may be acquired by the user device on a frequency other than the current frequency; predicting a first recommended frequency for the user device that is likely to have a higher capacity than the current frequency; instructing the user device to scan the first recommended frequency; receiving an indication from the user device that the first recommended frequency has a higher capacity than the current frequency; and communicating with the user device on the first recommended frequency.

Predictive model for high-capacity frequencies in mobile broadband

Presented here is a method to predict whether a user of a wireless telecommunication network will report a problem or issue associated with the wireless telecommunication network. A processor can obtain multiple key performance indicators (KPIs) describing a user experience with the wireless telecommunication network. The processor can calculate at least a daily value of each KPI according to a rule specific to the KPI. The processor can create an image representing a value of each KPI, where a first axis of the image identifies the KPI, and where a second axis of the image represents the daily value of the KPI. The processor can predict whether the user of the wireless telecommunication network will report the problem by providing the image to a machine learning model and receiving a prediction from the machine learning model whether the user of the wireless telecommunication network will report the problem.

Predicting whether a user of a wireless telecommunication network will report a problem

Presented here is a method to predict whether a user of a wireless telecommunication network will report a problem associated with the wireless telecommunication network. A processor can obtain multiple key performance indicators (KPIs) describing a user experience with the wireless telecommunication network. The processor can calculate at least a daily value of each KPI according to a rule specific to the KPI. The processor can create an image representing a color-coded value of each KPI, where a first axis of the image identifies the KPI, and where a second axis of the image represents the daily value of the KPI. The processor can predict whether the user of the wireless telecommunication network will report the problem by providing the image to a machine learning model and receiving a prediction from the machine learning model whether the user of the wireless telecommunication network will report the problem.

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Bellevue, WA, United States of America

Vikas Ranjan