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    QUALITY-OF-SERVICE MECHANISMS WITH OVERLAPPING BASIC SERVICE SET
    11.
    发明申请
    QUALITY-OF-SERVICE MECHANISMS WITH OVERLAPPING BASIC SERVICE SET 有权

    公开(公告)号：US20250039774A1

    公开(公告)日：2025-01-30

    申请号：US18359711

    申请日：2023-07-26

    Applicant: Cisco Technology Inc.

    Inventor： Al W. Dumdei ,  Malcolm Smith ,  Brian Donald Hart

    IPC: H04W48/16 ,  H04W28/02 ,  H04W28/24 ,  H04W72/566

    Abstract: The present disclosure is directed to detecting, by a first wireless endpoint device associated with a first basic service set (BSS) neighboring a neighboring overlapping BSS (OBSS), a received data packet from a second wireless endpoint device associated with the OBSS, upon determining the received data packet satisfies a first OBSS threshold and a high-priority data packet is in queue for transmission by the first wireless endpoint device, determining whether the received data packet satisfies a second OBSS threshold, maintaining the high-priority data packet in queue if the received data packet satisfies a second OBSS threshold, and transmitting the high-priority data packet by the first wireless endpoint device if the received data packet does not satisfy the second OBSS threshold.

    Using Machine Learning Techniques to Predict Device Buffer Status
    12.
    发明申请
    Using Machine Learning Techniques to Predict Device Buffer Status 有权

    公开(公告)号：US20240381166A1

    公开(公告)日：2024-11-14

    申请号：US18465380

    申请日：2023-09-12

    Applicant: Cisco Technology, Inc.

    Inventor： Robert Barton ,  Matt Silverman ,  Jerome Henry ,  Indermeet Gandhi ,  Malcolm Smith

    IPC: H04W28/02

    Abstract: Devices, systems, methods, and processes are described herein that can reduce network communication overhead by reducing data that is sent. This reduction in data can be achieved by reducing the requests for data related to the buffers of one or more network devices. This buffer status data reduction can be achieved by predicting the current status of the buffers in other network devices. When the buffer predictions are sufficient, the need to request or poll for buffer status reports can be reduced. The buffer predictions can be evaluated through one or more confidence levels. These buffer predictions and confidence levels can be generated by one or more machine learning processes that are configured to operate on one or more network devices. Based on the confidence levels of the predictions, the number of buffer status report polls and of any unsolicited buffer service reports can be reduced.

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