Sprint Communications Comapny L.p.

T-mobile Innovations LLC

T-mobile Usa, Inc.

Lance Paul Lukens

Kyle C Allen

Jay Dean Bluhm

Hui-Lin Chang

Bernard Robert Kingsley

Daqi Li

Shawn Derek Wallace

Wei Huang

Vikas Ranjan

Piradee Nganrungruang

Anandajothi Muttayane

Wayne Alexander Thompson

Anselmo Myungsup Shim

Leland John Garris

Diksha Agarwal

Farhana Sharmin Munnee

Systems and methods for making telecommunication network predictions on a granular geographic area-scale. The method can include receiving network key performance indicators (KPIs) associated with a plurality of user equipment (UEs) and then parsing the network KPIs into a plurality of geographic area-based categories corresponding to network KPIs obtained by cell towers or UEs located within a given geographic area, like a zip code. The method can further include combining the geographic area-based categories into clusters determined based on one or more similarities in the network KPIs of each, and predicting a most-influential one of the network KPIs within at least one of the clusters via a machine learning (ML) model. In response, the method can then cause an indicator to be generated at a user interface, the indicator indicating the most-influential one of the network KPIs within the at least one of the clusters.

Telecommunications network predictions based on machine learning using aggregated network key performance indicators

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

Anomalies in network traffic are detected using machine learning. A plurality of machine learning models is employed to determine whether there are anomalies in network traffic of an MPLS (Multiprotocol Label Switching) network that can affect the performance of devices in the network. A first machine learning model is trained on network traffic passed through network tunnels of a plurality of routers in the network. A second machine learning model is trained on router-specific network traffic passed through router-specific network traffic for a subset of the network tunnels associated with a particular router. The first machine learning model is employed to determine a network anomaly, and the second machine learning model is employed to determine a router-specific anomaly. A router error is identified when both a network anomaly and a router-specific anomaly are determined. An indication of the router error is communicated to a computing device.

Network anomaly detection using machine learning models

A cell site maintenance system. The system comprises a plurality of data stores, a processor, a memory coupled to the processor, and a part replacement prediction application stored in memory. When executed by the processor the application extracts features based on data read from the data stores, executes a plurality of part machine learning models, where each part machine learning model analyses some of the extracted features by a multi-label classification model trained for a specific part associated with that part machine learning model and each part machine learning model outputs a probability that the associated part can fix the cell site associated with the trouble ticket, and, based on a probability output by a part machine learning model associated with a first part exceeding a predefined threshold, determining that the first part is to be pulled from a part inventory to make ready for a service truck.

Cell site repair part prediction machine learning system

Methods and systems for facilitating placement of a small cell in a network are provided. A small cell may be positioned such that it is communicatively coupled to a macro cell to provide a relay backhaul to a wireless network. The small cell may be configured to provide feedback of some form when the small cell is moved from a suitable location, to facilitate desired placement and use of the small cell. The feedback may be in the form of a change in functionality of the one or more feedback components.

Facilitating desired placement of a small cell

Methods and systems for facilitating placement of a small cell in a network are provided. A small cell may be positioned such that it is communicatively coupled to a macro cell to provide a relay backhaul to a wireless network. The small cell may be configured to provide feedback of some form when the small cell is moved from a suitable location, to facilitate desired placement and use of the small cell. The feedback may be in the form of reduced or changed operation of a speaker.

Methods and systems for facilitating placement of a small cell in a network are provided. A small cell may be positioned such that it is communicatively coupled to a macro cell to provide a relay backhaul to a wireless network. The small cell may be configured to provide feedback of some form when the small cell is moved from a desired location, to facilitate desired placement and use of the small cell. The feedback may be in the form of reduced or changed operation of an associated device, such as a fan, a speaker, and/or a sign, or in the form of one or more communications over the network.

Growing community of inventors

Templeton, CA, United States of America

Lance Paul Lukens