QUERY-BASED CHANNEL STATE INFORMATION FEEDBACK DECODING FOR CROSS-NODE MACHINE LEARNING

    公开(公告)号:US20250062810A1

    公开(公告)日:2025-02-20

    申请号:US18450821

    申请日:2023-08-16

    Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network node, decoder configuration information associated with a transmitter neural network configured to be used to generate at least one latent vector corresponding to one or more computation tasks of a plurality of computation tasks associated with a query-based cross-node machine learning system. The UE may receive, from the network node, query configuration information associated with a query-based decoder. The UE may transmit, to the network node and based at least in part on instantiation of the transmitter neural network by the UE, the at least one latent vector. Numerous other aspects are described.

    TWO STAGE MACHINE LEARNING BASED CHANNEL STATE FEEDBACK

    公开(公告)号:US20240421875A1

    公开(公告)日:2024-12-19

    申请号:US18335726

    申请日:2023-06-15

    Abstract: Methods, systems, and devices for wireless communication are described. A user equipment (UE) may select a non-Discrete Fourier Transform (non-DFT) codebook of a set of non-DFT codebooks associated with a channel state feedback message. The UE may determine a set of singular vectors associated with the non-DFT codebook based on a first machine learning model, where the set of singular vectors corresponds to a subspace associated with the non-DFT codebook. The UE may compress the non-DFT codebook, the set of singular vectors, or both, based on a second machine learning model. The UE may transmit the channel state feedback message including the compressed non-DFT codebook, the compressed set of singular vectors, or both.

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