Methods and Systems for Multi-Model, Multi-Layer Perceptron Based Non-Linear Interference Management in Multi-Technology Communication Devices
    2.
    发明申请
    Methods and Systems for Multi-Model, Multi-Layer Perceptron Based Non-Linear Interference Management in Multi-Technology Communication Devices 审中-公开
    多技术通信设备中多模式,多层感知器的非线性干扰管理方法与系统

    公开(公告)号:US20160072543A1

    公开(公告)日:2016-03-10

    申请号:US14849532

    申请日:2015-09-09

    CPC classification number: H04B1/40 H04B1/123 H04B1/525 H04B15/00

    Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multi-layer perceptron neural network with Hammerstein structure by dividing an aggressor signal into real and imaginary components, augmenting the components by weight factors, executing a linear combination of the augmented components, and executing a nonlinear sigmoid function for the combined components at a hidden layer of multi-layer perceptron neural network to produce a hidden layer output signal. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce real and imaginary components of an estimated jammer signal. A linear filter function may be executed for the components of the jammer signal, and to produce a nonlinear interference estimate used to cancel the nonlinear interference of a victim signal.

    Abstract translation: 各种实施例包括用于在多技术无线通信设备的并发通信期间消除非线性干扰的方法和装置。 可以使用具有Hammerstein结构的多层感知器神经网络来估计非线性干扰,通过将侵略者信号划分成实部和虚部,通过权重因子增加分量,执行增强分量的线性组合,以及执行非线性Sigmoid函数 组合成分在隐层多层感知神经网络中产生隐层输出信号。 在输出层,可以通过加权因子来增加隐层输出信号,并且增强的隐层输出信号可以被线性组合以产生估计的干扰信号的实部和虚部。 可以对干扰信号的分量执行线性滤波器功能,并且产生用于消除受害信号的非线性干扰的非线性干扰估计。

    Methods and Systems for Multi-layer Perceptron Based Non-Linear Interference Management in Multi-Technology Communication Devices
    3.
    发明申请
    Methods and Systems for Multi-layer Perceptron Based Non-Linear Interference Management in Multi-Technology Communication Devices 有权
    多技术通信设备中基于多层感知器的非线性干扰管理方法与系统

    公开(公告)号:US20160072590A1

    公开(公告)日:2016-03-10

    申请号:US14849528

    申请日:2015-09-09

    CPC classification number: H04B1/40 H04B1/123 H04B1/525 H04B15/00

    Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a multilayer perceptron neural network with Hammerstein structure by dividing an aggressor signal into real and imaginary components, augmenting the components by weight factors, executing a linear combination of the augmented components, and executing a nonlinear sigmoid function for the combined components at a hidden layer of multilayer perceptron neural network to produce a hidden layer output signal. At an output layer, hidden layer output signals may be augmented by weight factors, and the augmented hidden layer output signals may be linearly combined to produce real and imaginary components of an estimated jammer signal. A linear filter function may be executed for the components of the jammer signal, and to produce a nonlinear interference estimate used to cancel the nonlinear interference of a victim signal.

    Abstract translation: 各种实施例包括用于在多技术无线通信设备的并发通信期间消除非线性干扰的方法和装置。 可以使用具有Hammerstein结构的多层感知器神经网络来估计非线性干扰,通过将侵略者信号除以实部和虚部,通过权重因子增加分量,执行增强分量的线性组合,以及执行组合的非线性S形函数 组件在隐藏层的多层感知器神经网络中产生隐层输出信号。 在输出层,可以通过加权因子来增加隐层输出信号,并且增强的隐层输出信号可以被线性组合以产生估计的干扰信号的实部和虚部。 可以对干扰信号的分量执行线性滤波器功能,并且产生用于消除受害信号的非线性干扰的非线性干扰估计。

    Methods and Systems for Radial Basis Function Neural Network With Hammerstein Structure Based Non-Linear Interference Management in Multi-Technology Communications Devices
    4.
    发明申请
    Methods and Systems for Radial Basis Function Neural Network With Hammerstein Structure Based Non-Linear Interference Management in Multi-Technology Communications Devices 审中-公开
    基于Hammerstein结构的径向基函数神经网络方法与系统在多技术通信设备中的非线性干扰管理

    公开(公告)号:US20160071007A1

    公开(公告)日:2016-03-10

    申请号:US14849536

    申请日:2015-09-09

    CPC classification number: G06N3/08 H04B1/123 H04J11/005

    Abstract: The various embodiments include methods and apparatuses for canceling nonlinear interference during concurrent communication of multi-technology wireless communication devices. Nonlinear interference may be estimated using a radial basis function neural network with Hammerstein structure by executing a radial basis function on aggressor signals at a hidden layer of the radial basis function neural network with Hammerstein structure to obtain hidden layer outputs, augmenting aggressor signal(s) by weight factors and, executing a linear combination of the augmented output, at an intermediate layer to produce a combined hidden layer outputs. At an output layer, a linear filter function may be executed on the hidden layer outputs to produce an estimated nonlinear interference used to cancel the nonlinear interference of a victim signal.

    Abstract translation: 各种实施例包括用于在多技术无线通信设备的并发通信期间消除非线性干扰的方法和装置。 可以使用具有Hammerstein结构的径向基函数神经网络来估计非线性干扰,通过在具有Hammerstein结构的径向基函数神经网络的隐层处对侵略者信号执行径向基函数以获得隐层输出,增强侵略者信号, 并且在中间层执行增强输出的线性组合以产生组合的隐层输出。 在输出层,可以在隐层输出上执行线性滤波函数,以产生用于消除受害信号的非线性干扰的估计的非线性干扰。

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