Behavior modeling method of power amplifier based on clock recurrent neural network

Abstract

The invention discloses a behavior modeling method of a power amplifier based on a clock recurrent neural network. The problems that an ordinary neural network model is iterated many times, and the long-term memory effect performance is poor are solved. By means of the characteristics that output of a recurrent neural network is not only related to real time input but also related to historical input, the memory effect of the power amplifier is described. On the basis, the weight of an ordinary recurrent neural network hidden layer is divided into multiple modules, each module has its own cycle, the weight of the module is only updated in its own cycle, and the number of weight updates is reduced to accelerate training of the neural network model. Accordingly, the non-linear features and memory effect of the power amplifier can be well described, and the high precision is achieved.

Description

technical field [0001] The invention relates to a power amplifier behavior modeling method based on a clock cycle neural network, belonging to the technical field of nonlinear system modeling and analysis applications. Background technique [0002] The power amplifier is an important module of the transmitter and is a complex nonlinear system. In order to make the power amplifier work with high efficiency, the transistors in the power amplifier mostly work in the near-saturation region or even the cut-off region, so the power amplifier often produces serious nonlinear distortion, and because of the influence of the equivalent reactance of the device, the power amplifier will produce memory effect. [0003] The modeling methods of power amplifiers can be divided into physical modeling and behavioral modeling. Physical modeling needs to know the specific structure inside the circuit and a proficient grasp of circuit knowledge to establish; while behavioral modeling only need...

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Problems solved by technology

[0006] In order to solve the deficiencies in the prior art, the object of the present invention is to provide a power amplifier behavior modeling method based on a clock cycle neural network, which solves the problem that the traditional neural network model only performs well in describing short-term memory effects, and is not good for long-term memory effects. Describes poorly behaved problems, and is good at describing power amplifier nonlinearities and memory effects

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