ML MODEL TRAINING PROCEDURE
    83.
    发明申请

    公开(公告)号:US20220377844A1

    公开(公告)日:2022-11-24

    申请号:US17323242

    申请日:2021-05-18

    Abstract: This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for an ML model training procedure. A network entity may receive a trigger to activate an ML model training procedure based on at least one of an indication from an ML model repository or a protocol of the network entity. The network entity may transmit an ML model training request to activate the ML model training at one or more nodes. The one or more nodes may be associated with a RAN that may transmit, based on receiving the ML model training request, ML model training results indicative of a trained ML model. In aspects, an apparatus, such as a UE, may train the ML model based on an ML model training configuration received from the RAN, and transmit an ML model training report indicative of the trained ML model.

    ADVERTISING INTERNET CONNECTION QUALITY
    85.
    发明申请

    公开(公告)号:US20200213910A1

    公开(公告)日:2020-07-02

    申请号:US16235371

    申请日:2018-12-28

    Abstract: This disclosure provides systems, devices, apparatus and methods, including computer programs encoded on storage media, for wireless communication. In one aspect, an apparatus for wireless communication may include a processing system configured to generate a first frame including an indication of an internet connection quality between the apparatus and a network, and a first interface configured to output the first frame for transmission to one or more wireless nodes that are unassociated with the apparatus.

    NEIGHBOR CELL LIST
    86.
    发明申请
    NEIGHBOR CELL LIST 审中-公开

    公开(公告)号:US20190245614A1

    公开(公告)日:2019-08-08

    申请号:US16331945

    申请日:2017-09-01

    Abstract: The disclosure relates in some aspects to enabling a user terminal (UT) to obtain information about nearby cells and any beams generated by nearby cells. For example, a network can send a neighbor cell list to UTs, where the list identifies the cells in that neighborhood and provides information about any beams generated by those cells. Thus, a UT can learn the neighboring beams/cells that the UT can reselect to if the current beam/cell becomes weak. In some aspects, the UE can learn the attitude (e.g., pitch, roll, yaw, or any combination thereof) profile of neighboring satellites as well as the pointing angles and the ON-OFF schedules of their beams. In some aspects, the UT can learn a start angle and a span for a satellite and use this information to identify a satellite the UT can reselect to if the current beam/cell becomes weak.

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