MACHINE LEARNING FOR CHANNEL ESTIMATION AGAINST DIVERSE DOPPLER EFFECTS

    公开(公告)号:US20240187285A1

    公开(公告)日:2024-06-06

    申请号:US18286276

    申请日:2021-04-15

    CPC classification number: H04L25/0254 H04L25/0226 H04W64/006

    Abstract: Embodiments of the present disclosure relate to machine learning for channel estimation against diverse Doppler effects. In this solution, the first device receives user data in a slot from a second device via a channel between the first device and the second device, the user data includes a reference symbol. Then, the first device performs, with a NN, a first channel estimation for the channel based on information on velocity of a second device and a result of a second channel estimation for the channel. The second channel estimation is performed based on the received reference symbol. After that, the first device transmits, to the second device, an indication of a result of the first channel estimation. With this solution, channel estimation is able to be performed in diverse scenarios (e.g., diverse Doppler scenarios, high-velocity scenarios and the like) with low overheads in reference signals (e.g., 1 DMRS), thereby reducing network consumption.

    METHODS, DEVICES, APPARATUSES, AND MEDIUM FOR OPTICAL COMMUNICATION

    公开(公告)号:US20220239371A1

    公开(公告)日:2022-07-28

    申请号:US17579987

    申请日:2022-01-20

    Abstract: The method includes receiving, at a first optical communication device, feedback information on training of a neural network from at least one second optical communication device, the neural network configured to process a signal received from the first optical communication device, the feedback information at least including a training performance indication for training of the neural network conducted at the at least one second optical communication device; updating, based on the feedback information, a first initial parameter value set for the neural network maintained at the first optical communication device, to obtain a second initial parameter value set for the neural network; and transmitting the second initial parameter value set to at least one further second optical communication device, for training of the neural network to be conducted at the at least one further second optical communication device based on the second initial parameter value set.

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