Verizon Patent and Licensing Inc.

Cellco Partnership

Carol Becht

Ye Ouyang

Krishna Pichumani Iyer

Gopinath Venkatasubramaniam

Christopher M Schmidt

Le Su

Lily H Zhu

Abhinav Prasad

Benjamin Lowe

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 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.

Methods and systems for evaluating voice over wi-fi (VoWiFi) call quality

A recursive algorithm may be applied to group cells in a service network into a small number of clusters. For each of the clusters, different regression algorithms may be evaluated, and a regression algorithm generating a smallest error is selected. A total error for the clusters may be identified based on the errors from the selected regression algorithms and from degrees of separation associated with the cluster. If the total error is greater than a threshold value, the cells may be grouped into a larger number of clusters and the new clusters may be re-evaluated. A key performance indicator (KPI) may be estimated for a cell based on a regression algorithm selected for the cluster associated with the cell. A resources may be allocated to the cell based on the KPI value.

Modeling network performance and service quality in wireless networks

Methods and systems for evaluating voice over Wi-Fi (VoWiFi) call quality

An exemplary profiling system builds a two-layer mapping model for a mobile network. The two-layer mapping model establishes a causal relationship between a plurality of application behavior indicators and network resource usage within the mobile network by defining a first mapping relationship between the plurality of application behavior indicators and a plurality of network performance indicators representative of network traffic that passes through the mobile network, and a second mapping relationship between the plurality of network performance indicators and network resource usage within the mobile network. Corresponding systems and methods are also described.

Methods and systems for profiling network resource usage by a mobile application

A method includes receiving over-the-air (OTA) performance test data measured for a first set of pass computing devices and for a second set of fail computing devices, measuring a first set of training data for the first and second set of computing devices during a plurality of KPI tests, measuring a second set of training data for a particular computing device, determining which ones of the plurality of KPIs qualify as clustering features, and determining a first set of KPI centers, a second set of KPI centers, and a third set of KPI centers, determining a first and a second dissimilarity distance separating the first set and the second set of computing devices from the particular device, respectively. The method further includes determining whether the first dissimilarity distance is greater than second dissimilarity distance to qualify the particular computing device to pass the OTA test.

Evaluating device antenna performance and quality

A device may receive an initial set of network parameter values, associated with cells of a cellular network, that are measured or calculated based on communications associated with the cells of the cellular network. The device may determine a set of feature values, associated with the cells of the cellular network, using the initial set of network parameter values. The device may cluster the cells of the cellular network into a first group of clusters using a first clustering technique, and may cluster the cells of the cellular network into a second group of clusters using a second clustering technique. The device may cluster the cells of the cellular network into a final group of clusters based on the first group of clusters and the second group of clusters, and may output information associated with the final group of clusters of the cells of the cellular network.

Cellular network cell clustering and prediction based on network traffic patterns

An exemplary method includes a voice quality assessment system receiving, from a mobile telephone operating on a mobile network, a test record that includes network indicator data and radio frequency ('RF') data both measured by the mobile telephone while the mobile telephone is at a location within the mobile network, and applying the network indicator data and the RF data to a voice quality model built using data provided by a plurality of mobile telephones operating on the mobile network in order to generate a voice quality score that quantifies a quality of voice communications for the mobile telephone at the location within the mobile network. Corresponding systems and methods are also described.

Methods and systems for determining a voice quality score for a mobile telephone

Systems and methods for analyzing and forecasting network traffic are disclosed. In some implementations, multiple points are received. Each point represents a time value and a network traffic value. For each point in the multiple points, the network traffic value is decomposable into a trend component, a seasonality component, a burst component, and a random error component. A trend equation is generated for calculating the trend component based on the time value. A seasonality equation is generated for calculating the seasonality component based on the time value. A burst equation is generated for calculating the burst component based on the time value. A random error equation is generated for calculating the random error component. A network traffic equation is generated for calculating the network traffic value based on the time value by combining the trend equation, the seasonality equation, the burst equation, and the random error equation.

Analyzing and forecasting network traffic

Systems and methods for evaluating device quality are disclosed. Some implementations include receiving, operational parameters for a mobile device, where the operational parameters include data related to service disturbances in a plurality of areas of operational performance of the mobile device, computing, for each dimension of a plurality of dimensions, a curve indicative of a relationship between a device quality index (DQI) and operational parameters of the mobile device, where each dimension corresponds to a particular area of operational performance of the mobile device, identifying cliff points in each dimension of a multi-dimensional surface fitted to each computed curve, where each cliff point corresponds to a reduction in a user's quality of experience in a particular area of operational performance, and projecting the identified cliff points in each dimension of the multi-dimensional surface to determine a horizontal space in the multi-dimensional surface corresponding to a region of acceptable DQIs.

Evaluating device quality

Systems and methods for estimating network capacity or performance are disclosed. In some implementations, a target network resource is selected for evaluation. Key performance indicators for computing a target network resource value for the target network resource are determined. The determined key performance indicators are filtered out of a set of key performance indicators. A data set is gathered. The data set comprises multiple points. Each point indicates a key performance indicator value for at least one of the determined key performance indicators and the target network resource value. A calculation module is trained to calculate the selected target network resource value based on key performance indicator values included in the gathered data set. The calculation module is tested using a testing data set. The testing data set has a same probability distribution of key performance indicator values and target network resource values as the gathered data set.

Estimating long term evolution network capacity and performance

Systems and methods for evaluating cellular voice call quality are disclosed. In some implementations, training data points are received. Each data point includes a call quality value and values of key performance indicators (KPIs) related to call quality. For each KPI, a linear relationship, having a goodness-of-fit value, between the KPI and the call quality value is determined using the multiple data points. Compulsory KPIs are selected based on the goodness-of-fit values. The training data points are separated into clusters based on the compulsory KPI values and the quality values. For each cluster, a mathematical relationship for calculating the call quality within the cluster is determined based on the one or more compulsory KPIs. A module is generated for predicting the call quality value by combining the determined mathematical relationships.

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Boonton, NJ, United States of America

Carol Becht