Verizon Patent and Licensing Inc.

Other

Brian A Ward

Matthew Kapala

Jason A Birr

Hector A Garcia Crespo

Ricardo Francisco Borges

Dean P Jarski

Sudha Subramaniam

Brent D Segner

Hector Alejandro Garcia Crespo

Richard S Delk

Scott Anthony Townley

Timothy E Coyle

Gaurav B Patel

Christian Winter

Sankaran Ramanathan

Xia Li

Katrina Adams

Deborah Lynn Liske

A device may receive uplink physical resource block (PRB) interference data from a base station, and may process the uplink PRB interference data, with a first machine learning model, to generate same sector similarity score data for the base station. The device may process the same sector similarity score data, with a second machine learning model, to identify and classify at least one antenna issue of the base station, and may create sector-carrier pair data, from the same sector similarity score data, based on the at least one antenna issue. The device may process the sector-carrier pair data, with a third machine learning model, to identify issues that span sector carriers of the base station, and may calculate an issue status and alignment score based on the issues and the same sector similarity score data. The device may perform actions based on the issue status and alignment score.

Systems and methods for utilizing machine learning models to determine radio access network antenna performance impact

A device may determine that a first geographical area satisfies an area threshold. The first geographical area includes an estimated location of a source of interference associated with base stations. A first plurality of vertices, of the first geographical area, are based on locations of the base stations. The device may determine a second plurality of vertices. The second plurality of vertices are closer to the estimated location than the first plurality of vertices. The device may provide information regarding the second plurality of vertices and may obtain magnitude information indicating magnitudes of the interference measured at the second plurality of vertices. The device may determine a third plurality of vertices based on the magnitude information. The device may provide information regarding a second geographical area, formed by the third plurality of vertices, to cause a search of the second geographical area to identify the source of the interference.

Systems and methods for determining a source of interference based on current measurements of a measurement device

A system described herein may provide a technique for the analysis of antennas in a wireless network, and the ensuing detection of misaligned antennas. Images reflecting measures of interference associated with different antennas may be analyzed based on structural similarity index ('SSIM') or other similarity analysis techniques, and antennas may be paired according to this analysis. The paired antennas may be compared to a known configuration. Mismatches may indicate misalignment or other malfunctions, based on which automated remedial measures (e.g., antenna realignment) may be performed.

Systems and methods for analyzing and adjusting antenna pairs in a multiple-input multiple-output ('MIMO') system using image scoring techniques

An interference detection system in a network determines that an unknown radio frequency (RF) interference source that causes RF interference experienced by a first wireless station is a persistent RF interference source over a plurality of time intervals in a selected time period. A predicted interference source location is identified for each time interval in the selected time period. An aggregated predicted interference source location is calculated based on the identified one or more predicted interference source locations.

Systems and methods for identifying a source of radio frequency interference in a wireless network

A device may receive input data associated with a wireless network, and may extract data from the input data to generate extracted data. The device may create PRB images based on the extracted data, and may process the PRB images, with a first model, to associate labels with each of the PRB images. The device may process the labels and the PRB images, with a second model, to identify potential issues associated with the PRB images, and may process data identifying the potential issues associated with the PRB images, with a third model, to compress the data identifying the potential issues into an array. The device may process the array, with a fourth model, to determine probability scores associated with the potential issues, and may select a potential issue with a greatest probability score as a detected issue. The device may perform actions based on the detected issue.

Systems and methods for utilizing machine learning and neural network models to identify issues in wireless networks

A device may receive antenna alert data associated with antennas of a plurality of base stations of a network, and may process the antenna alert data, with a correlation model, to generate a correlation heatmap matrix. The device may utilize a change point detection model to process the antenna alert data, associated with the correlation heatmap matrix, to determine change point metrics, and may calculate scores for the antenna alert data associated with the correlation heatmap matrix and the change point metrics. The device may perform one or more corrective actions based on the scores.

Systems and methods for determining radio access network antenna poor performance subscriber impact

Systems described herein may allow for the intelligent configuration of containers onto virtualized resources. Different configurations may be generated based on the simulation of alternate placements of containers onto nodes, where the placement of a particular container onto a particular node may serve as a root for several branches which may themselves simulate the placement of additional containers on the node (in addition to the container(s) indicated in the root). Once a set of configurations are generated, a particular configuration may be selected according to determined selection parameters and/or intelligent selection techniques.

Systems and methods for virtual machine resource optimization using tree traversal techniques representing alternate configurations

An interference detection system in a network identifies a first wireless station that has experienced radio frequency (RF) interference from an unknown source on at least one physical resource block (PRB) by determining that a key performance indicator (KPI) for the at least one PRB on the first wireless station has a value indicative of interference. The interference detection system identifies one or more second wireless stations that have experienced similar interference on the at least one PRB. A plurality of estimated interference source locations are determined based at least on geographic locations of the first wireless station and the one or more second wireless stations. Determining the plurality of estimated interference source locations further comprises generating a boundary based on the geographic locations of the first wireless station and the one or more second wireless stations and selecting a plurality of estimated interference source locations within the boundary.

A device obtains configuration parameters for multiple network elements of a network and receives first scores that assess a level of efficiency and a level of isolation for each of the multiple network elements. The device identifies a network element of the multiple network elements for modifying the identified network element's configuration based on the obtained configuration parameters and the received first scores.

System and method for establishing network element personality profiles using AI/ML

A device may receive location information associated with a fixed service satellite station (receiver) configured to communicate via a first radio frequency (RF) band. The device may obtain historical communication data associated with the location and relating to previous communications conducted via a second RF band, of user equipment (UEs). The historical communication data may include historical signal power measurements of the previous communications. The device may adjust the historical signal power measurements to model estimated signal power measurements relating to a UE to communicating via a first RF band. The device may determine, based on the estimated signal power measurements, an interference score associated with a probability of a UE interfering with the receiver when communicating, via a first RF band, with a base station.

Systems and methods for detecting interference probability within a radio frequency band

An interference detection system in a network identifies a first wireless station that has experienced radio frequency (RF) interference from an unknown source and identifies one or more second wireless stations that have experienced similar interference. A plurality of estimated interference source locations are scored based on a comparison of estimated interference to observed interference at the one or more second wireless stations. A predicted interference source location is identified based on the scored plurality of estimated interference source locations. It is determined whether the unknown interference source is a persistent interference source over a selected time period, wherein the predicted interference source location is identified for each interval in the selected time period. The predicted interference source locations for each interval in the selected time period are retrieved and an aggregated predicted interference source location is calculated based on the retrieved predicted interference source locations.

An interference detection system in a network identifies a first wireless station that has experienced radio frequency (RF) interference from an unknown source on at least one physical resource block (PRB) and identifies one or more second wireless stations that have experienced similar interference on the at least one PRB. A plurality of estimated interference source locations are determined based at least on geographic locations of the first wireless station and the one or more second wireless stations. The plurality of estimated interference source locations are scored based on a comparison of estimate interference to observed interference at the one or more second wireless stations and a geographical map is generated based on the scored plurality of estimated interference source locations, wherein the geographical map includes indicia indicative of the relative scores of the plurality of estimated interference source locations.

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Fort Worth, TX, United States of America

Brian A Ward