DYNAMIC SPECTRUM ACCESS MODE BASED ON STATION CAPABILITIES

    公开(公告)号:US20230147828A1

    公开(公告)日:2023-05-11

    申请号:US17454340

    申请日:2021-11-10

    CPC classification number: H04W24/08 H04B17/345 H04L1/0013

    Abstract: Dynamic spectrum access mode based on station capabilities is provided by categorizing functionalities of Access Points (APs) and mobile stations (STA) in a wireless network; identifying interference induced by external signaling devices on channels in the wireless network; calculating an impact factor of the interference based on proximity of the external signaling devices to the wireless network, a pattern of the external signaling devices, and an extent of overlap with frequencies used by the external signaling devices and the wireless network; and in response to identifying a given STA that is paired with a given AP in the wireless network, wherein the given STA and the given AP are both categorized as being capable of both multilink communications and preamble puncturing, assigning network resources for the given STA to communicate with the given AP via one of multilink communications or preamble puncturing based on the impact factor of the interference.

    NETWORK ENVIRONMENT HEALTH MONITORING

    公开(公告)号:US20210377839A1

    公开(公告)日:2021-12-02

    申请号:US16888314

    申请日:2020-05-29

    Abstract: Network environment health monitoring is provided by receiving an alert indicating that a first station (STA) is experiencing a connection with a first Access Point (AP) below a quality threshold; identifying a set of APs connected to a shared network with the first AP within one hop of the first AP; aggregating signal metrics for the first STA from the first AP and each AP of the set of APs; identifying a cause for the connection performing below the quality threshold based on the signal metrics as aggregated; and performing a remediation strategy based on the cause as identified.

    NETWORK ENVIRONMENT HEALTH MONITORING
    8.
    发明公开

    公开(公告)号:US20240031900A1

    公开(公告)日:2024-01-25

    申请号:US18481034

    申请日:2023-10-04

    CPC classification number: H04W36/30 H04W36/0085 H04W36/0061

    Abstract: Network environment health monitoring is provided by receiving an alert indicating that a first station (STA) is experiencing a connection with a first Access Point (AP) below a quality threshold; identifying a set of APs connected to a shared network with the first AP within one hop of the first AP; aggregating signal metrics for the first STA from the first AP and each AP of the set of APs; identifying a cause for the connection performing below the quality threshold based on the signal metrics as aggregated; and performing a remediation strategy based on the cause as identified.

    FLEXIBLE RADIO ASSIGNMENT WITH BEAMSTEERING ANTENNAS

    公开(公告)号:US20230178890A1

    公开(公告)日:2023-06-08

    申请号:US17457330

    申请日:2021-12-02

    CPC classification number: H01Q3/36 H01Q25/002

    Abstract: Flexible radio assignment with beamsteering antennas is provided by controlling a plurality of Access Points (APs) including steerable antennas to each transmit a first plurality of discovery frames at a first beamwidth; controlling the plurality of APs to steer the steerable antennas at a second beamwidth, less than the first beamwidth, to a plurality of steering angles; controlling the plurality of APs to each transmit a second plurality of discovery frames at each steering angle of the plurality of steering angles; determining an overlap in radio coverage among the plurality of APs based on the first plurality and the second plurality of discovery frames; and identifying redundant radios based on the overlap in radio coverage; and reassigning the redundant radios from use for client transmissions to a secondary role.

    STATION MOVEMENT FLOW DRIVEN AUTOMATIC RF SITE GROUPING

    公开(公告)号:US20200304378A1

    公开(公告)日:2020-09-24

    申请号:US16358689

    申请日:2019-03-19

    Abstract: Techniques for dynamic RF site configuration are provided. Historical association data is collected from a plurality of access points in a physical environment, and a machine learning model is trained to predict future association events, based on the historical association data. Current association data is then collected from the plurality of access points, and at least one predicted association event is generated by processing the current association data using the trained machine learning model. The plurality of access points is allocated to a plurality of radio frequency (RF) sites based on the at least one predicted association event. Finally, at least one of the plurality of RF sites is configured based on the predicted association event.

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