GROUP-COMMON REFERENCE SIGNAL FOR OVER-THE-AIR AGGREGATION IN FEDERATED LEARNING

    公开(公告)号:US20230084883A1

    公开(公告)日:2023-03-16

    申请号:US17898180

    申请日:2022-08-29

    Abstract: Aspects presented herein may enable a network entity to configure a group of UEs to simultaneously transmit reference signals and to simultaneously transmit gradient vectors to the network entity, such that the network entity may receive the gradient vectors from the group of UEs as an aggregated gradient vector over the air. In one aspect, a base transmits, to a group of UEs, a configuration that configures the group of UEs to simultaneously transmit one or more group-common reference signals and to simultaneously transmit one or more gradient vectors associated with a federated learning procedure. The network entity receives, from the group of UEs, the one or more group-common reference signals and the one or more gradient vectors based on the configuration via multiple channels. The network entity calculates an average gradient vector based on the one or more group-common reference signals and the one or more gradient vectors.

    REPORTING POTENTIAL VIRTUAL ANCHOR LOCATIONS FOR IMPROVED POSITIONING

    公开(公告)号:US20230019644A1

    公开(公告)日:2023-01-19

    申请号:US17374694

    申请日:2021-07-13

    Abstract: Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) determines a positioning measurement of a first multipath component of a radio frequency (RF) signal transmitted by a transmission-reception point (TRP), determines a first additional positioning measurement of a second multipath component of the RF signal, determines a second additional positioning measurement of a third multipath component of the RF signal, and transmits a measurement report to a location server, the measurement report including at least the positioning measurement, the first additional positioning measurement, the second additional positioning measurement, and one or more parameters associated with the first additional positioning measurement and the second additional positioning measurement.

    ML MODEL TRAINING PROCEDURE
    279.
    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.

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