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    MACHINE LEARNING-BASED LINK ADAPTATION
    2.
    发明申请
    MACHINE LEARNING-BASED LINK ADAPTATION 有权

    公开(公告)号：US20210218483A1

    公开(公告)日：2021-07-15

    申请号：US17252976

    申请日：2018-09-28

    Applicant: Intel Corporation

    Inventor： Xiaoran Fang ,  Matthew Hayes ,  John P. Hogan ,  Shoujiang Ma ,  Mehrzad Malmirchegini ,  Rongzhen Yang ,  Hujun Yin

    IPC: H04B17/336 ,  H04W24/08 ,  H04L1/00

    Abstract: Aspects for machine learning-based link adaptation are described. For example, an apparatus can determine k-nearest neighbors (K-NNs) based on training data associated with the sub-band and on the signal to interference and noise ratio (SINR) of the sub-band. In aspects, the apparatus can identify a channel quality indicator (CQI) associated with the lowest error rate for the k-NNs and provide the identified CQI to a base station. In aspects, a neural network (NN) can provide labels for CQIs that indicate probability of choosing a CQI, and the CQI having highest probability will be provided to a base station. In aspects, a covariance matrix based on samples of a communication channel can be provided to a NN to determine a rank indicator (RI) corresponding to the channel, and channel state information associated with the (RI) can be sent to the base station. Other aspects are described.

    Machine learning-based link adaptation
    3.
    发明授权
    Machine learning-based link adaptation 有权

    公开(公告)号：US11637643B2

    公开(公告)日：2023-04-25

    申请号：US17252976

    申请日：2018-09-28

    Applicant: Intel Corporation

    Inventor： Xiaoran Fang ,  Matthew Hayes ,  John P. Hogan ,  Shoujiang Ma ,  Mehrzad Malmirchegini ,  Rongzhen Yang ,  Hujun Yin

    IPC: H04B17/336 ,  H04L1/00 ,  H04W24/08

    Abstract: Aspects for machine learning-based link adaptation are described. For example, an apparatus can determine k-nearest neighbors (K-NNs) based on training data associated with the sub-band and on the signal to interference and noise ratio (SINR) of the sub-band. In aspects, the apparatus can identify a channel quality indicator (CQI) associated with the lowest error rate for the k-NNs and provide the identified CQI to a base station. In aspects, a neural network (NN) can provide labels for CQIs that indicate probability of choosing a CQI, and the CQI having highest probability will be provided to a base station. In aspects, a covariance matrix based on samples of a communication channel can be provided to a NN to determine a rank indicator (RI) corresponding to the channel, and channel state information associated with the (RI) can be sent to the base station. Other aspects are described.

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