Abstract:
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.
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:
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.
Abstract:
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.
Abstract:
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.
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 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.
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 non-intrusive method of determining whether a mobile device is transmitting through a Wi-Fi router or a cellular network is described. The method includes monitoring messages relating to a wireless network. A subset of the messages including Create Session Request, and IMSI/MDN are received and later a subset of the messages including Delete Session Request, and the IMSI/MDN are received. The combination of the sets of received messages is used to determine whether the mobile device is transmitting through the Wi-Fi router or the cellular network, specifically based on the Create Session Request and the Delete Session Request.