FINE-TUNING OF MACHINE LEARNING MODELS ACROSS MULTIPLE NETWORK DEVICES

    公开(公告)号:US20240161012A1

    公开(公告)日:2024-05-16

    申请号:US18457116

    申请日:2023-08-28

    CPC classification number: G06N20/00

    Abstract: An apparatus, method and computer-readable media are disclosed for performing wireless communications. For example, a first network device can transmit, to one or more second network devices, configuration information associated with a trained machine learning model. The first network device can receive, from the one or more second network devices, information associated with a first fine-tuned machine learning model based on adaptation of parameters of the trained machine learning model. The first network device can further output, for transmission to one or more third network devices, configuration information associated with the first fine-tuned machine learning model.

    POSITIONING MODEL FAILURE DETECTION
    187.
    发明公开

    公开(公告)号:US20240019528A1

    公开(公告)日:2024-01-18

    申请号:US17813279

    申请日:2022-07-18

    CPC classification number: G01S5/02521 G01S5/0263

    Abstract: In an aspect, a method of wireless communication performed by a user equipment (UE) includes determining that a positioning model error instance has occurred during a positioning occasion based on 1) a position uncertainty associated with a first position estimate satisfying positioning uncertainty error criteria, wherein the first position estimate is obtained during the positioning occasion by applying a first positioning model to radio frequency fingerprint (RFFP) measurements, 2) a positioning mismatch between the first position estimate of the UE and a second position estimate of the UE satisfying position mismatch error criteria, wherein the second position estimate of the UE is obtained during the positioning occasion by performing a positioning technique that does not use the first positioning model, or 3) any combination thereof; and determining that the first positioning model has failed based on a number of positioning model error instances satisfying positioning model failure criteria.

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