Verizon Patent and Licensing Inc.

Cellco Partnership

Krishna Pichumani Iyer

Ye Ouyang

Le Su

Carol Becht

Christopher M Schmidt

Wenyuan Lu

Mauricio Pati Caldeira De Andrada

Amit Mahajan

Maulik Shah

Derek Wade Ohlarik

Ravikumar Pattabiraman

Hoo Dennis Ong

Shaun Robert Pola

Steven F Rice

Jacques Meekel

Khurram Abbas

Zhenyi Lin

Jogendra Yaramchitti

Greselda P Powell

Lily H Zhu

Joel Stephen Angiolillo

Abhinav Prasad

Dandan Wang

Graham David Sysko

Lun Yin

Yiming Chen

Namrata Prakash

Lin Tang

Garrison William Brainard

Sihao Goi

Dwight Ellis Woods

Mark Guerra

Dhruv Khettry

Yu Wan

A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.

Systems and methods for reduced resource utilization for event modeling

A device may include a memory storing instructions and processor configured to execute the instructions to receive information relating to a plurality of vehicles in an area. The device may be further configured to use a trained machine learning model to determine a likelihood of collision by one or more of the plurality of vehicles; identify one or more relevant vehicles of the plurality of vehicles that are in danger of collision based on the determined likelihood of collision; and send an alert indicating the danger of collision to at least one of the identified one or more relevant vehicles.

Systems and methods for machine learning based collision avoidance

A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Systems and methods to improve network performance in wireless networks using user mobility patterns

A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Systems and methods for evaluating a user experience in a network based on performance indicators

A system described herein may provide for the identification and configuration of a radiohead of a previously unknown type. A set of radio frequency ('RF') signals, encoded in a time and frequency domain and broadcast by the radiohead, may be identified. A set of synchronization signals may be identified in the RF signals. A set of candidate radioheads, associated with pilot signals that correspond to the identified synchronization signals, may be identified. Decoding techniques, associated with the candidate radioheads, may be used, based on the synchronization signals, to identify a Master Information Block ('MIB') in the RF signals, based on which the radiohead may be identified and configured.

Systems and methods for identifying and configuring a radiohead for a network simulation test platform

A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Network anomaly detection and network performance status determination

A Self-Organizing Network (SON) collects data pertaining to a first number of cells of a wireless network. The SON splits the collected data into a second number of groups, and, for each of the second number of groups, repeatedly set a third number of clusters to a different number between a low limit and a high limit. The SON, for each of the settings, clusters the cells into the third number of clusters and trains a deep neural network to perform a regression analysis on the third number of clusters. For each of the second number of groups, the SON also determines an optimum number of clusters based on the regression analyses, re-clusters the cells into the optimum number of clusters; and tunes engineering parameters based on the re-clustering to optimize performance of the wireless network and quality of experience pertaining to the wireless network.

Automated intelligent self-organizing network for optimizing network performance

A method, a device, and a non-transitory storage medium for estimating voice call quality include performing automatic speech recognition, for each of a plurality of voice calls, to generate recognized text for both an originating device acoustic signal and a receiving device acoustic signal. The recognized text for both the originating device acoustic signal and the receiving device acoustic signal are compared to the reference text to identified recognition errors and a voice call quality score for each of the originating device acoustic signal and the receiving device acoustic signal are determined. A correlation between the network conditions and the voice call quality scores is then determined.

Methods and systems for evaluating voice call quality

A device may receive cell property data associated with a cell in a mobile network and performance data associated with the cell. The device may determine whether the performance data associated with the cell satisfies a performance threshold. The device may identify, based on determining that the performance data associated with the cell satisfies the performance threshold, one or more impacted cells, in the mobile network, associated with the cell. The device may determine one or more antenna adjustment parameters based on at least the cell property data associated with the cell, the performance data associated with the cell, and performance data associated with the one or more impacted cells. The device may perform, based on the one or more antenna adjustment parameters, an action in connection with at least one of an antenna associated with the cell or another antenna associated with the one or more impacted cells.

Intelligent optimization of cells in a mobile network

A method to enhance a subjective quality of experience for an application may include receiving network performance data, the data representing at least one observable application characteristic, and the subjective quality of experience (QoE) survey data. The method may further include generating at least one perception model which relates the data representing at least one observable application characteristic and the network performance data, and determining a QoE model which relates the subjective QoE survey data and the data representing at least one observable application characteristic. The method may further include inverting the at least one perception model and the QoE model to obtain a relationship between network performance parameters and the at least one observable application characteristic, and adjusting network parameters based on the at least one inverted perception model and inverted QoE model.

Application quality of experience evaluator for enhancing subjective quality of experience

Compatibility and/or compliance testing for a wireless cellular network may be performed using a testing system that includes a device that implements multiple base stations in a single hardware device. Additionally, a network simulation server may simulate a core portion of the wireless cellular network. Test cases, such as test cases defined using a scripting language, may be received and the test cases may be interpreted to obtain configuration information for the testing system. Based on the configuration information, the base stations and the simulated network devices may be initially configured. After configuration, and based on the test case, a UE may be controller to interact with the simulated network in a manner implements the desired compatibility and/or compliance tests.

Testing and validation of user equipment for a cellular network

A wireless companion device that supports an Embedded Universal Integrated Circuit Card receives a logging request from a wireless communication device. The wireless companion device applies to a remote provisioning server for logging information that corresponds to remote provisioning of the eUICC. The wireless companion device receives the logging information and routes at least a portion to the wireless communication device.

Apparatus and method of local profile assistant eUICC logging

A method, a device, and a non-transitory storage medium provide receiving a plurality of voice call quality values and values for a plurality key performance indicators (KPIs) related to voice over Wi-Fi voice call quality; selecting a subset of the plurality of KPIs based on a correlation between each KPI and the voice call quality value; performing a plurality of discrete regression analyses based on the subsets of the plurality of KPIs and the voice call quality values to generate a plurality of regression results; determining an accuracy for each of the plurality of regression results; assigning weights to each of the plurality of regression results based on the determined accuracies; and combining the plurality of regression results using the assigned weights to generate a final combined estimated VoWiFi voice call quality algorithm that accurately predicts the voice call quality value based on values for the selected subset of the plurality of KPIs.

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Basking Ridge, NJ, United States of America

Krishna Pichumani Iyer