METHOD AND APPARATUS FOR VALIDATING MACHINE-LEARNING-BASED PREDICTIONS OF LINE-OF-SIGHT INDICATOR VALUES

    公开(公告)号:US20250150186A1

    公开(公告)日:2025-05-08

    申请号:US18931323

    申请日:2024-10-30

    Abstract: Test equipment (TE) may, while in a test configuration, cause transmission of a first reference signal for receipt by a communication device under test (DUT). The TE may receive a first line-of-sight indicator value as determined by the DUT in accordance with a method having a known accuracy. While in the test configuration, the TE may cause transmission of a second reference signal for receipt by the DUT. The TE may receive a second line-of-sight indicator value as determined by the DUT based at least in part on a trained machine learning (ML) model. The TE may determine an error value between the first line-of-sight indicator value and the second line-of-sight indicator value. Based at least in part on the error value, the TE may determine whether the trained ML model passes a conformance test related to estimation of line-of-sight indicator values by the trained ML model.

    AI/ML MODEL TEST MECHANISM
    3.
    发明申请

    公开(公告)号:US20250070902A1

    公开(公告)日:2025-02-27

    申请号:US18798373

    申请日:2024-08-08

    Abstract: Example embodiments of the present disclosure are related to artificial intelligence/machine learning (AI/ML) model test. A first apparatus transmits test configuration information to a second apparatus, the test configuration information indicating a test mode of an AI/ML model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator. The first apparatus receives, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI/ML model. The first apparatus determines a test result for the AI/ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI/ML model is validated.

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