System and method for access point selection and scoring based on machine learning

    公开(公告)号:US10820221B2

    公开(公告)日:2020-10-27

    申请号:US16410284

    申请日:2019-05-13

    Abstract: A device may receive information that identifies a first set of parameter values associated with a first set of access points. The first set of access points may be associated with a set of known access point quality scores. The device may generate a model based on the set of known access point quality scores and the first set of parameter values. The device may receive information that identifies a second set of parameter values associated with a second set of access points. The device may determine a set of access point quality scores, for the second set of access points, based on the second set of parameter values and the model. The device may provide information to permit an action to be performed in association with the second set of access points.

    APPLICATION QUALITY OF EXPERIENCE EVALUATOR FOR ENHANCING SUBJECTIVE QUALITY OF EXPERIENCE

    公开(公告)号:US20170244777A1

    公开(公告)日:2017-08-24

    申请号:US15047728

    申请日:2016-02-19

    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.

    Methods and Systems for Profiling Network Resource Usage by a Mobile Application
    6.
    发明申请
    Methods and Systems for Profiling Network Resource Usage by a Mobile Application 有权
    通过移动应用程序分析网络资源使用情况的方法和系统

    公开(公告)号:US20170026949A1

    公开(公告)日:2017-01-26

    申请号:US14805361

    申请日:2015-07-21

    Abstract: 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.

    Abstract translation: 示例性分析系统为移动网络构建两层映射模型。 双层映射模型通过定义多个应用行为指示符之间的第一映射关系和表示网络流量的多个网络性能指标,来建立多个应用行为指示符与移动网络内的网络资源使用之间的因果关系, 通过移动网络,以及多个网络性能指示符与移动网络内的网络资源使用之间的第二映射关系。 还描述了相应的系统和方法。

    Modeling network performance and service quality in wireless networks

    公开(公告)号:US10187899B2

    公开(公告)日:2019-01-22

    申请号:US15941552

    申请日:2018-03-30

    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.

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