SYNCHRONIZATION SIGNAL BLOCK LOCATION FOR FRAME BASED EQUIPMENT MODE IN CELLULAR COMMUNICATIONS

    公开(公告)号:US20230171748A1

    公开(公告)日:2023-06-01

    申请号:US17758438

    申请日:2020-02-14

    CPC classification number: H04W72/0446

    Abstract: This disclosure provides systems, methods and apparatus for cellular communications. In one aspect, a UE determines an FFP of a cell signal for the UE in a frame based equipment mode, determines one or more SSB positions in the cell signal based on the FFP, and performs radio management of the UE based at least in part on the one or more invalid SSB candidate positions. Performing radio management may include using one or more SSB positions in the FFP exclusive of the one or more invalid SSB candidate positions. Radio management may include PDSCH rate matching, radio link monitoring or measurement, or radio resource management. In some implementations, the one or more SSB positions are SSB positions after a first eight SSB positions in an FFP. In some other implementations, the one or more SSB positions are SSB positions that at least partially overlap an idle period between FFPs.

    MACHINE LEARNING APPROACH TO MITIGATE HAND BLOCKAGE IN MILLIMETER WAVE SYSTEMS

    公开(公告)号:US20230170967A1

    公开(公告)日:2023-06-01

    申请号:US17537197

    申请日:2021-11-29

    CPC classification number: H04B7/0695 H04B1/3838 H04B7/0639 H04B7/0634

    Abstract: The present disclosure provides for beam selection by a mobile device based on a sensor state of the mobile in order to mitigate hand blockage. The mobile device detects, during operation of the mobile device, a sensor state caused by a hand of an operator on the mobile device. The mobile device selects for communication, while operating in the sensor state, a refined beam set corresponding to the sensor state based on reference signals. The mobile device trains a machine learning model based on a set of training data including pairs of the sensor state and the refined beam set to select a first set of beam weights from a second set of beam weights based on a detected sensor state. The mobile device selects for communication a hand blockage state including the first set of beam weights for a current detected sensor state based on the machine learning model.

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