Spirent Communications, Inc.

Meenal Agarwal

Peretz Moshe Feder

Sandra Ramos Thuel

Marius Adamut

Brion Noah Feinberg

Peter Peng

Ovidiu Reghis

Thomas Russo

The disclosed technology teaches dispersion analysis of individual UE devices, measuring the span of cellular locations in a 5G network over which the bulk of a user's data, voice minutes and text messaging usage take place. Dispersion analysis also measures the span of cellular locations over which the bulk of a user's transactions and failed transactions take place, providing insights into camping behavior on specific cellular locations and hot spot formation. The technology disclosed includes new collection and filtering of location data to pinpoint cellular communication challenges encountered by users as well as system improvement and remediation opportunities. Additionally, this technology enables reporting of the detected behavior and related hot spots formation analysis with identifiers of UE mobile devices involved. The technology also applies to 3G 4G and future mobile networks and can be utilized for a group of UE devices selected for analysis, by device, geography, or affinity.

Measuring and using dispersion for user mobility analytics to improve management of cellular systems

The disclosed technology takes into account mobility behavior and interactions with a serving cellular network of a UE mobile device, when determining network interaction parameters for the UE, including providing an identifier of the UE to a NWDAF component that subscribes to receive notices that report changed or changing events, including a change in connectivity to any cell for any UE and any service usage on the cell and performs a dispersion analysis by analyzing the incoming notices to determine multiple dispersion indices as the UE mobile device changes its selection of a base station or a cell or a slice. Also included are network elements receiving the dispersion analysis for the UE mobile device and performing a policy update and/or a radio access network (RAN) decision for the UE mobile device based on at least the dispersion analysis. The technology also applies to 3G, 4G and future mobile networks.

Updating policy and radio access network decisions based on dispersion analytics of cellular systems

The disclosed technology teaches alerting a network operator to deteriorated wireless network conditions with granularity and reduced false alerts, including receiving tracked performance indicators (KPIs) for multiple components of voice, data and messaging and accessing a mapping for combining the performance indicators into quality sub-indicators for individual users and into key quality indicators (KQI) for the individual users. This includes smoothing the current KQI measures of performance over time to reduce the impact of brief transitory events, accessing a weekly profile of KQIs, and comparing KQIs for a current time window to past KQIs for a corresponding time window for the individual users operating mobile devices in part of the cellular network that is a focus of interest. Based on the comparing, the disclosed technology includes generating alerts that report out-of-range current KQIs within the focus of interest that are persistently out-of-range for longer than a configurable time.

Predicting subscriber experience based on QoE

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Marlboro, NJ, United States of America

Meenal Agarwal