MACHINE LEARNING COMPONENT MANAGEMENT
    81.
    发明公开

    公开(公告)号:US20240064574A1

    公开(公告)日:2024-02-22

    申请号:US17821388

    申请日:2022-08-22

    CPC classification number: H04W28/18 H04W8/24 H04W24/02

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit, to a network node, UE capability information associated with at least one machine learning component. The UE may receive, from the network node and based on the UE capability information, configuration information corresponding to the at least one machine learning component. The UE may generate a first machine learning output based on the machine learning component. The UE may perform a communication task based on the first machine learning output. Numerous other aspects are described.

    TECHNIQUES FOR DOWNLOADING MODELS IN WIRELESS COMMUNICATIONS

    公开(公告)号:US20240056798A1

    公开(公告)日:2024-02-15

    申请号:US17886381

    申请日:2022-08-11

    CPC classification number: H04W8/245 H04W48/10 H04W72/1289

    Abstract: Aspects described herein relate to receiving, from a network node, a list of supported models or model structures (MS) identifiers (IDs) per machine learning function name (MLFN) or machine learning feature (MLF) at the network node, updating a capability at the UE to an updated capability based on the list of supported models or MS IDs per MLFN or MLF at the network node, and downloading, at the UE and from a model repository, one or more models or MSs per MLFN or MLF based on the updated capability and available resources at the UE. Other aspects relate to transmitting the list of supported models or MS IDs and configuring use of a model or MS ID for a particular MLFN or MLF.

    MOBILITY ROBUSTNESS OPTIMIZATION ENHANCEMENT USING FALLBACK INDICATIONS FOR INTER-SYSTEM HANDOVER REPORTS

    公开(公告)号:US20240049094A1

    公开(公告)日:2024-02-08

    申请号:US18351851

    申请日:2023-07-13

    CPC classification number: H04W36/305

    Abstract: A user equipment (UE) disconnects from next generation radio access network (NG-RAN) base station (BS) and receives, from the NG-RAN BS, a fallback indication. The UE then attempts to connect to an evolved-universal mobile telecommunications system terrestrial radio access network (E-UTRAN) BS. The UE determines that communication with the E-UTRAN BS was not established and identifies an alternate BS. After establishing communication with the alternate BS, the UE generates and transmits a report to the alternate BS including the fallback indication and information relating to the failed connection attempt with the E-UTRAN BS. The report is then conveyed to the NG-RAN BS for optimization of future fallback procedures.

    NETWORK HANDLING OF PRIMARY SECONDARY CELL GROUP CELL (PSCELL) CHANGE

    公开(公告)号:US20240049074A1

    公开(公告)日:2024-02-08

    申请号:US18365345

    申请日:2023-08-04

    CPC classification number: H04W36/0079 H04W36/08

    Abstract: Methods, devices, and mechanisms for detecting and reporting successful secondary node and/or primary secondary cell group cell (PScell) changes are provided. In one example, a method of wireless communication performed by a first network unit comprises: transmitting, to a second network unit, an indication of a primary secondary cell group cell (PScell) change associated with a user equipment (UE); transmitting, based on the indication, a SPC configuration; and receiving a SPC report, wherein the SPC report is based on the SPC configuration and SPC information associated with the UE.

    ML MODEL TRAINING PROCEDURE
    89.
    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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