Distortion probing reference signal configuration

    公开(公告)号:US11949618B2

    公开(公告)日:2024-04-02

    申请号:US18194479

    申请日:2023-03-31

    CPC classification number: H04L5/0048 H04B17/309

    Abstract: Methods, systems, and devices for wireless communications are described. A configuration for a reference signal used to determine a non-linear behavior of transmission components at a transmitting device may be determined. The configuration for the reference signal may be determined based on signaling transmitted by the transmitting device, signaling transmitted by a device that receives the reference signal, or both. Additionally, or alternatively, the configuration for the reference signal may be determined based on a configuration of other signals transmitted by the transmitting device prior to or concurrently with the transmission of the reference signal. The determined configuration may be used to generate and transmit the reference signal or to determine a configuration of a received reference signal. In both cases, a non-linear response of transmission components at the transmitting device may be determined based on the reference signal.

    Hybrid closed-loop multiple-input multiple-output and transparent diversity schemes

    公开(公告)号:US11943018B2

    公开(公告)日:2024-03-26

    申请号:US18160875

    申请日:2023-01-27

    Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) having partially coherent antennas may be configured for simultaneous transmissions on groups of antennas. To achieve the benefits of simultaneous transmissions using groups of antenna that are partially coherent, without having the transmissions affect each other, the UE may apply a hybrid closed-loop multiple-input multiple-output (MIMO) scheme among each antenna in the antenna groups where phase coherence can be maintained. Following the hybrid closed-loop MIMO scheme, the UE may apply a transparent diversity scheme across each antenna of the groups. Alternatively, the UE may first apply the transparent diversity scheme and next apply the hybrid closed-loop MIMO scheme. By applying a hybrid closed-loop MIMO scheme, and a transparent diversity scheme, the UE may fully realize its resources and contribute to an improved spatial diversity for a MIMO system.

    TECHNIQUES FOR DETERMINING CHANNEL STATE INFORMATION USING A NEURAL NETWORK MODEL

    公开(公告)号:US20230421223A1

    公开(公告)日:2023-12-28

    申请号:US18253427

    申请日:2021-01-29

    CPC classification number: H04B7/0626 H04B17/3913 H04B7/0456

    Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive a first indication of a first number of antenna ports for which the UE may report channel state information (CSI), and a second indication of a second number of antenna ports on which the UE may measure CSI reference signals (CSI-RSs). The second number may be less than the first number. The UE may receive a third indication of one or more neural networks to be used by the UE for determination of the CSI associated with the first number. The UE may determine the CSI using the one or more neural networks and using measurements made by the UE on the second number as inputs to the one or more neural networks. The UE may transmit a report including the CSI associated with the first number determined via the one or more neural networks.

    WEIGHTED AVERAGE FEDERATED LEARNING BASED ON NEURAL NETWORK TRAINING LOSS

    公开(公告)号:US20230297825A1

    公开(公告)日:2023-09-21

    申请号:US17672533

    申请日:2022-02-15

    CPC classification number: G06N3/08 H04W24/02

    Abstract: A method of wireless communication by a user equipment (UE) includes computing updates to an artificial neural network as part of an epoch of a federated learning process. The updates include gradients or updated model parameters. The method also includes recording a training loss observed while training the artificial neural network at the epoch of the federated learning process. The method further includes transmitting the updates to a federated learning server that is configured to aggregate the gradients based on the training loss.

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