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    Machine-learning-based RF optimization
    3.
    发明授权
    Machine-learning-based RF optimization 有权

    公开(公告)号：US10039016B1

    公开(公告)日：2018-07-31

    申请号：US15622147

    申请日：2017-06-14

    Applicant: Verizon Patent and Licensing Inc.

    Inventor： Bryan Christopher Larish ,  Said Soulhi

    IPC: H04W4/00 ,  H04W24/02 ,  G06N99/00 ,  H04W24/04

    CPC classification number: H04W24/02 ,  G06N20/00 ,  H04W24/04

    Abstract: A method is provided for obtaining reference signal measurements over a structured interface to support RF optimization via machine learning. The method, performed by a network device, includes identifying a target cluster of cell towers for a radio access network (RAN); generating a model for collecting RAN measurements from mobile communication devices in the target cluster; and sending the model via a structured reference point to client applications on the mobile communication devices. The model may direct collection of and sending of the RAN measurements by the client applications. The method may further include receiving, via the structured reference point, the RAN measurements from the client applications based on the model; and aggregating the RAN measurements to represent aspects of the target cluster based on the model.

    Systems and methods for managing network performance based on defining rewards for a reinforcement learning model
    6.
    发明授权
    Systems and methods for managing network performance based on defining rewards for a reinforcement learning model 有权

    公开(公告)号：US11063841B2

    公开(公告)日：2021-07-13

    申请号：US16683966

    申请日：2019-11-14

    Applicant: Verizon Patent and Licensing Inc.

    Inventor： Said Soulhi ,  Bryan Christopher Larish

    IPC: H04L12/24 ,  H04L12/26

    Abstract: A device may receive network policies of a network, and network performance data identifying KPIs of the network, and may generate an embedded space of reconstructed data that is embedded in an original space that includes the KPIs. The device may calculate reconstruction errors based on differences between the reconstructed data and the network performance data, and may calculate a convex hull of the original space. The device may calculate a convex hull of the embedded space, and may determine reward metrics based on the reconstruction errors, the convex hull of the original space, and the convex hull of the embedded space. The device may define performance baselines associated with portions, and may generate a new reward for a portion based on a particular reconstruction error, a particular convex hull of the embedded space, and a particular performance baseline. The device may perform actions based on the new reward.

    METHOD AND SYSTEM FOR ANOMALY DETECTION AND NETWORK DEPLOYMENT BASED ON QUANTITATIVE ASSESSMENT
    7.
    发明申请
    METHOD AND SYSTEM FOR ANOMALY DETECTION AND NETWORK DEPLOYMENT BASED ON QUANTITATIVE ASSESSMENT 审中-公开

    公开(公告)号：US20200267174A1

    公开(公告)日：2020-08-20

    申请号：US16797192

    申请日：2020-02-21

    Applicant: Verizon Patent and Licensing Inc.

    Inventor： Said Soulhi ,  Bryan Christopher Larish

    IPC: H04L29/06 ,  H04L12/26 ,  H04L12/24

    Abstract: A method, a device, and a non-transitory storage medium provide a validation and anomaly detection service. The service includes quantitatively assessing latent space data representative of network performance data, which may be generated by a generative model, based on quantitative values pertaining to quantitative criteria. The quantitative criteria may include Hausdorff distances, divergence, joint entropy, and/or total correlation. The service further includes generating geogrid data for services areas of deployed network devices and service areas for prospective and new deployments based on selected latent space data and corresponding network performance data.

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