Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.
Abstract:
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.