Nonlinear neural network model for modeling wide band RF (Radio Frequency) power amplifier

Abstract

The invention discloses a nonlinear neural network model for a modeling wide band RF (Radio Frequency) power amplifier. The model comprises an input layer, a hidden layer and an output layer, wherein the input data of the input layer comprises advance items x (n+1), |x (n+1)|3, ..., |x (n+1)|<2Q+1>, aligning items x(n), |x(n)|, |x(n)|[3], ..., |x (n)|<2Q+1>, and delay items x (n-1), ..., x (n-M[1]), |x (n-1)|, |x (n-1)|, ..., |x (n-M[2])|, ..., |x (n-1)|<2Q+1>, ..., |x (n-M[Q+2]|<2Q+1>, wherein the x (n+1) is base band complex data of an input end of RF power amplifier at current time, and the output of the output layer is y(n). The nonlinear neutral network has the advantages that a generalized memory effect (memory effects at the delay time and the advance time shall be considered) is considered based on a super-strong memory effect and a strong static nonlinearity of the modeling RF power amplifier; meanwhile, an input signal of an input layer does not only comprises a base band signal, but also comprises a model of a base band complex signal and a high power of the model, and the output signal of the output layer is a plural signal, therefore the modeling precision is higher and can be improved by 5dB in comparison with a real time delay neural network model.

Description

technical field [0001] The invention relates to a neural network model, in particular to a neural network model for modeling the nonlinearity of broadband radio frequency power amplifiers. Background technique [0002] The successful application of neural network model in pattern recognition, signal processing, system identification and control has attracted many researchers in the field of RF power amplifier modeling to study it. The neural network models currently used to model RF power amplifiers are mainly divided into two categories: the first category is a neural network model that does not consider the memory effect, and the second category is a neural network model that considers the memory effect. Because the first type of neural network model does not consider the memory effect, its modeling ability is greatly reduced when the broadband signal drives the power amplifier, and it is rarely used at present. The second type of neural network model considers the memory...

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Problems solved by technology

The existing real time delay neural network model includes an input layer, a hidden layer and an output layer, and the input signal in the transfer function of the input layer includes the in-phase component (Iin) and the quadrature component (Qin) of the baseband complex signal at the input end of the radio frequency power amplifier , the in-phase component (Iin) and the quadrature component (Qin) are calculated and processed sequentially through the transfer function of the hidden layer and the transfer function of the output layer, and the output at the output layer is the in-phase component of the baseband complex signal at the output end of the RF power amplifier at the current moment (Iout) and quadrature component (Qout), so the input signal of the real time-delay neural network model only considers the memory effect at the lag time, when modeling the super memory effect and strong static nonlinearity of the RF power amplifier, it is proposed Mode accuracy is low

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