Learning in communication systems

    公开(公告)号:US12124937B2

    公开(公告)日:2024-10-22

    申请号:US17295296

    申请日:2018-11-22

    CPC classification number: G06N3/045 H04B1/16 H04B1/0003

    Abstract: An apparatus, method and computer program is described including initialising trainable parameters of a receiver of a transmission system, wherein the receiver includes a demodulation module for demodulating received symbols, a quantization module for generating quantized versions of the demodulated symbols and a decoder for generating a decoded output derived from the quantized versions of the demodulated symbols, wherein the demodulation module has at least some trainable weights and the quantization module has at least some trainable weights; receiving a first training sequence of messages at the receiver; obtaining or generating a loss function; and updating at least some of the trainable parameters of receiver based on the loss function, wherein updating at least some of the trainable parameters of receiver includes updating at least some of the trainable weights of the demodulation module and updating at least some of the trainable weights of the quantization module.

    Regularization of covariance matrix and eigenvalue decomposition in a MIMO system

    公开(公告)号:US11387871B2

    公开(公告)日:2022-07-12

    申请号:US17407698

    申请日:2021-08-20

    Abstract: A technique is provided including at least obtaining a noise and interference covariance matrix, performing scaling of the noise and interference covariance matrix, determining a sum of diagonal elements of the scaled noise and interference covariance matrix, performing a first spectral shift by the sum to the scaled noise and interference covariance matrix to obtain a first spectral shifted matrix, performing eigenvalue decomposition to the first spectral shifted matrix to obtain an eigenvalue matrix, performing a second spectral shift by the sum to the eigenvalue matrix, limiting the eigenvalue matrix to a diagonal matrix, and obtaining a new noise and interference covariance matrix adding the scaled noise and interference covariance matrix and eigenvalue pairs that are obtained based, at least partly, on the performed eigenvalue decomposition.

    EFFICIENT LINEAR DETECTION IMPLEMENTATION FOR MASSIVE MIMO

    公开(公告)号:US20200021348A1

    公开(公告)日:2020-01-16

    申请号:US16434377

    申请日:2019-06-07

    Inventor: Ori Shental

    Abstract: Per given time instance, K samples b are acquired from a signal r, which is based at least on K transmitted symbols x and a transfer matrix H of a communication channel, and a linear detection matrix A of a size K×K is acquired, which is based at least on the transfer matrix H (S101). For the K samples b and the linear detection matrix A, at most K(K−1) tentative parameters b˜ and at most K(K−1) tentative parameters A˜ are iteratively calculated (S102).It is checked whether or not the tentative parameters b˜ and A˜ have converged (S103). If b˜ and A˜ have converged, K estimation values x{circumflex over ( )} are decided for the K transmitted symbols x based on b˜ and A˜ (S104). If b˜ and A˜ have not converged, it is returned to the iteratively calculating b˜ and A˜ for the K samples b.

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