INTERFERENCE DATA COLLECTION WITH BEAM INFORMATION FOR ML-BASED INTERFERENCE PREDICTION

    公开(公告)号:US20240089769A1

    公开(公告)日:2024-03-14

    申请号:US17932191

    申请日:2022-09-14

    CPC classification number: H04W24/10 H04B17/336 H04W16/28 H04W56/001

    Abstract: An apparatus for wireless communication at a UE is provided. The apparatus is configured to receive a configuration to report interference measurement information indicating interference measurements for each interference measurement resource of a set of interference measurement resources and Rx beam information used by the UE for performing interference measurements on each interference measurement resource of the set of interference measurement resources. The apparatus is configured to receive a set of interference measurement reference signals on the set of interference measurement resources, and to measure interference on each interference measurement resource of the set of interference measurement resources to obtain the interference measurement information. Each interference measurement is through one Rx beam of a set of Rx beams. The apparatus is configured to transmit, in response to the received set of interference reference signals on the set of interference measurement resources, the interference measurement information, and corresponding Rx beam information.

    MACHINE LEARNING MODEL POSITIONING PERFORMANCE MONITORING AND REPORTING

    公开(公告)号:US20230354247A1

    公开(公告)日:2023-11-02

    申请号:US18188638

    申请日:2023-03-23

    CPC classification number: H04W64/00 H04W4/029

    Abstract: Disclosed are techniques for wireless communication. In an aspect, a network entity receives a provide location information message from a user equipment (UE), the provide location information message including one or more positioning estimates derived by the UE during one or more positioning inference occasions of a machine learning model, wherein the machine learning model is applied to one or more measurements of a wireless channel between the UE and a network node during each of the one or more positioning inference occasions, and transmits a performance report indicating a performance of the machine learning model at least in deriving the one or more positioning estimates during the one or more positioning inference occasions.

    MACHINE LEARNING FOR BEAM PREDICTIONS WITH CONFIDENCE INDICATIONS

    公开(公告)号:US20230353264A1

    公开(公告)日:2023-11-02

    申请号:US17661543

    申请日:2022-04-29

    CPC classification number: H04B17/373 G06N3/08 H04B17/318 H04W24/08

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a signal. The UE may determine, based at least in part on a machine learning component, a predicted communication metric and a confidence indication, the machine learning component comprising a machine learning model, and wherein determining the predicted communication metric and the confidence indication comprises: receiving, by the machine learning model, an input that comprises an input metric and an error measurement corresponding to the input metric; and providing, by the machine learning model, and based at least in part on a machine learning function and the input, the predicted communication metric and the confidence indication. The UE may perform a wireless communication task based at least in part on the predicted communication metric and the confidence indication. Numerous other aspects are described.

    BEAM SELECTION USING OVERSAMPLED BEAMFORMING CODEBOOKS AND CHANNEL ESTIMATES

    公开(公告)号:US20230318881A1

    公开(公告)日:2023-10-05

    申请号:US17658025

    申请日:2022-04-05

    CPC classification number: H04L25/0254 H04L25/0242 H04B7/0456 H04B7/0617

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a first network node may receive, from a second network node, codebook information that indicates a plurality of beams associated with an oversampled transmitter network node beamforming codebook. The first network node may transmit a beam selection report that indicates at least one suggested transmission beam associated with the oversampled transmitter network node beamforming codebook, wherein the beam selection report is based at least in part on a channel estimate that is obtained without obtaining beam measurements associated with beams that are associated with the oversampled transmitter network node beamforming codebook. Numerous other aspects are described.

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