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    Beam-based machine learning-enabled RFFP positioning
    474.
    发明授权
    Beam-based machine learning-enabled RFFP positioning 有权

    公开(公告)号：US11843993B2

    公开(公告)日：2023-12-12

    申请号：US17457718

    申请日：2021-12-06

    Applicant: QUALCOMM Incorporated

    Inventor： Mohammed Ali Mohammed Hirzallah ,  Srinivas Yerramalli ,  Taesang Yoo ,  Rajat Prakash ,  Xiaoxia Zhang

    IPC: H04W4/029 ,  H04B7/06 ,  H04B17/318

    CPC classification number: H04W4/029 ,  H04B7/0617 ,  H04B17/318

    Abstract: Aspects presented herein may enable an ML module to associate RF fingerprints with beam directions and/or beam features to improve the uniqueness of RF fingerprints. In one aspect, network entity may receive, from one or more wireless devices, a plurality of first RF fingerprints, each of the plurality of first RF fingerprints being associated with at least one directional feature and a location. The network entity may receive a request to determine a position of a UE based on at least one second RF fingerprint associated with the UE or captured by the UE. The network entity may estimate the position of the UE based at least in part on matching the at least one second RF fingerprint to at least one of the plurality of first RF fingerprints.

    ML model training procedure
    476.
    发明授权
    ML model training procedure 有权

    公开(公告)号：US11818806B2

    公开(公告)日：2023-11-14

    申请号：US17323242

    申请日：2021-05-18

    Applicant: QUALCOMM Incorporated

    Inventor： Rajeev Kumar ,  Eren Balevi ,  Taesang Yoo ,  Xipeng Zhu ,  Gavin Bernard Horn ,  Shankar Krishnan ,  Aziz Gholmieh

    IPC: H04W16/22 ,  H04W88/08 ,  G06N20/00 ,  G06F18/214

    CPC classification number: H04W88/08 ,  G06F18/214 ,  G06N20/00

    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.

    LAYER-BY-LAYER TRAINING FOR FEDERATED LEARNING
    480.
    发明公开
    LAYER-BY-LAYER TRAINING FOR FEDERATED LEARNING 审中-公开

    公开(公告)号：US20230316062A1

    公开(公告)日：2023-10-05

    申请号：US17697751

    申请日：2022-03-17

    Applicant: QUALCOMM Incorporated

    Inventor： Eren Balevi ,  Taesang Yoo ,  Tao Luo ,  Srinivas Yerramalli ,  Junyl Li ,  Hamed Pezeshki

    IPC: H04B7/06 ,  G06N3/04 ,  G06N3/08

    CPC classification number: G06N3/08 ,  G06N3/04 ,  H04B7/0626

    Abstract: Methods, systems, and devices for wireless communications are described. A network entity may transmit an indication of neural network weights to one or more user equipments (UEs). The neural network weights may be for one or more shared layers of a federated learning neural network. The UEs may train a personalized layer of the neural network using the weights and data at the UEs. The UEs may transmit layer updates to the network entity. The network entity may train the neural network based on the updates. The UEs may send a transmission to the network entity that may be processed according to the neural network at the UEs and the network entity.

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