HARDWARE ALLOCATION IN RFIC BASED ON MACHINE LEARNING

    公开(公告)号:US20230297803A1

    公开(公告)日:2023-09-21

    申请号:US17834845

    申请日:2022-06-07

    CPC classification number: G06K19/0723 G06N3/08

    Abstract: A system and method for configuring an RF network based on machine learning. In some embodiments, the method includes: receiving, by a first neural network, a first state and a first state transition, the first state including: one or more identifiers for available active ports, and a set of available connections between two or more circuit elements, each of the circuit elements being one of: (1) a first circuit type, (2) a second circuit type that operatively connects a circuit element of the first circuit type to one of the available active ports, and (3) the available active ports; and generating, by the first neural network, a first estimated quality value, for the first state transition.

    SYSTEMS AND METHODS FOR NOISE POWER ESTIMATION IN WIRELESS COMMUNICATIONS

    公开(公告)号:US20250132955A1

    公开(公告)日:2025-04-24

    申请号:US18888144

    申请日:2024-09-17

    Abstract: A system and a method are disclosed for noise power estimation in wireless communications. In some embodiments, the method includes: receiving a reference signal; generating a first channel estimate, based on the reference signal; calculating a first noise power estimate; calculating a corrected noise power estimate by applying a multiplicative correction based on a linear minimum mean square error filter weight to the first noise power estimate; receiving a transmission; and decoding the transmission, based on the corrected noise power estimate.

    APPARATUS FOR AND METHOD OF CHANNEL QUALITY PREDICTION THROUGH COMPUTATION OF MULTI-LAYER CHANNEL QUALITY METRIC
    6.
    发明申请
    APPARATUS FOR AND METHOD OF CHANNEL QUALITY PREDICTION THROUGH COMPUTATION OF MULTI-LAYER CHANNEL QUALITY METRIC 有权
    通过计算多层通道质量标准的通道质量预测的方法和方法

    公开(公告)号:US20160261316A1

    公开(公告)日:2016-09-08

    申请号:US15040437

    申请日:2016-02-10

    CPC classification number: H04B7/0413 H04B17/309 H04L1/203

    Abstract: An apparatus and method for a transceiver are provided. The apparatus for the transceiver includes a multiple input multiple output (MIMO) antenna; a transceiver connected to the MIMO antenna; and a processor configured to measure channel gain Hk, based on the received signal, where k is a sample index from 1 to K, Hk is an m×n matrix of complex channel gain known to the transceiver, measure noise variance σ2 of a channel, calculate a per-sample channel quality metric q(Hk, σ2) using at least one bound of mutual information; reduce a dimension of a channel quality metric vector (q(H1, σ2), . . . , q(HK, σ2)) by applying a dimension reduction function g(.); and estimate a block error rate (BLER) as a function of a dimension reduced channel quality metric g(q(H1, σ2), . . . , q(HK, σ2)).

    Abstract translation: 提供了一种用于收发器的装置和方法。 用于收发器的装置包括多输入多输出(MIMO)天线; 连接到MIMO天线的收发器; 以及处理器,被配置为基于接收信号来测量信道增益H k,其中k是从1到K的采样索引,Hk是收发器已知的复信道增益的m×n矩阵,测量信道的噪声方差σ2 ,使用互信息的至少一个界限来计算每采样信道质量度量q(Hk,σ2); 通过应用维数减小函数g(。)来减小信道质量度量向量(q(H1,σ2),...,q(HK,σ2))的维数。 并且作为尺寸减小的信道质量度量g(q(H1,σ2),...,q(HK,σ2))的函数来估计块错误率(BLER)。

    APPARATUS AND METHOD FOR MODELING RANDOM PROCESS USING REDUCED LENGTH LEAST-SQUARES AUTOREGRESSIVE PARAMETER ESTIMATION

    公开(公告)号:US20180196906A1

    公开(公告)日:2018-07-12

    申请号:US15465181

    申请日:2017-03-21

    CPC classification number: G06F17/5036 G06F17/5072 G06F17/5081

    Abstract: An apparatus and method for modelling a random process using reduced length least-squares autoregressive parameter estimation is herein disclosed. The apparatus includes an autocorrelation processor, configured to generate or estimate autocorrelations of length m for a stochastic process, where m is an integer; and a least-squares (LS) estimation processor connected to the autocorrelation processor and configured to model the stochastic process by estimating pth order autoregressive (AR) parameters using LS regression, where p is an integer much less than m. The method includes generating, by an autocorrelation processor, autocorrelations of length m for a stochastic process, where m is an integer; and modelling the stochastic process, by a least-squares estimation processor, by estimating pth order autoregressive (AR) parameters by least-squares (LS) regression, where p is an integer much less than m.

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