LOCALIZATION VIA MACHINE LEARNING BASED ON PERCEIVED CHANNEL PROPERTIES AND INERTIAL MEASUREMENT UNIT SUPERVISION

    公开(公告)号:US20240372636A1

    公开(公告)日:2024-11-07

    申请号:US18481655

    申请日:2023-10-05

    Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A sequence of data records is accessed, each data record comprising wireless channel measurements and inertial measurement unit (IMU) data. Known position information corresponding to at least a first data record is accessed. A first sequence of positions is determined by processing the sets of IMU data and known position information using a forward operation. A second sequence of positions is determined by processing the sets of IMU data and known position information using a backward operation. An IMU adjustment parameter is generated using the first and second sequences of positions. A pseudo-label is generated for a second data record using the IMU adjustment parameter and the sets of IMU data. A machine learning model is trained, using the second data record and the pseudo-label, to predict positions using one or more wireless channel measurements.

    DATA COLLECTION AND TRAINING FOR A NETWORK POSITIONING MODEL

    公开(公告)号:US20240337721A1

    公开(公告)日:2024-10-10

    申请号:US18295817

    申请日:2023-04-04

    CPC classification number: G01S5/02524 G01S5/0236 H04L5/0048 H04W64/00

    Abstract: A network node may receive a set of sounding reference signals (SRSs) from a wireless device. The network node may measure the set of SRSs. The network node may output at least one of a set of estimated positioning labels, training associated information, or labeling assistance information associated with the set of measured SRSs for a positioning model. In one example, the network node may output the data by training the positioning model based on at least one of the set of estimated positioning labels, the training associated information, the labeling assistance information, or the set of measured SRSs. In another example, the network node may output the data by transmitting, for a network entity, at least one of the set of estimated positioning labels, the training associated information, or the set of measured SRSs for training the positioning model.

    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.

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