Cellco Partnership

Verizon Patent and Licensing Inc.

Gopinath Venkatasubramaniam

Christopher M Schmidt

Ye Ouyang

Carol Becht

Yuk Lun Li

Manuel Enrique Caceres

Mauricio Pati Caldeira De Andrada

Nanjun Qian

A first network device executing a device simulation tool receives a selection of a second network device in a communications network. The first network device receives a selection of at least one use case, scenario or error condition associated with operation of the second network device or the communications network, and executes a network simulation of the communications network based on the at least one use case, scenario or error condition. The first network device generates a call flow diagram, which involves the selected second network device, based on the executed network simulation, and provides a graphical display of the generated call flow diagram.

Network device and call simulation tool

A charger and a mobile device exchange messages indicating that high wattage charging is supported over a bidirectional communication channel. After the message exchange, the charger sends a controlled output voltage and current to the mobile device over a cable connected between the charger and the mobile device. The mobile device takes an input voltage measurement that corresponds to the controlled output voltage after being passed through the cable. The charger or mobile device may then calculate a resistance of the cable based on a difference between the controlled output voltage and the input voltage measurement. Based on the resistance of the cable, the controlled voltage and current used to charge a battery of the mobile device is adjusted in order to avoid exceeding capacity of the cable.

Weak component detection for charging capacity control

Techniques described herein may use perceptual quality measurement techniques to test and/or evaluate communication sessions between mobile devices. A method may include receiving, by a mobile device and as part of an audio or video call with a second mobile device, a streamed version of a reference audio or video file; and reading a version of the reference audio or video file that was previously stored locally to the mobile device. The method may further include calculating, based on a comparison of the streamed version of the reference audio or video file to the version of the reference audio video file that was stored locally, a perceptual quality score for the streamed version of the reference audio or video file.

Mobile device perceptive audio and video quality analysis using onboard test signals

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.

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Bridgewater, NJ, United States of America

Gopinath Venkatasubramaniam