Power amplifier predistortion method of Hammerstein model based on fuzzy neural network

Abstract

A power amplifier predistortion method of a hammerstein model based on the fuzzy-neural network mainly comprises a non-linear subsystem without memory and a linear subsystem with memory. The non-linear subsystem without memory is composed of a fuzzy-neural network with a one-order Sugeno FIS structure, for compensating the static margin and the phase distortion characteristic of the power amplifier, while the linear subsystem with memory is composed of a finite impulse response (FIR) filter for compensating the memory effect of the power amplifier. Combined with the indirect learning structure, the parameters of the fuzzy-neural network are recognized by combining the Least square and the Back-propagation, and the linear FIR filter coefficient is determined by the Least square. The predistortion project not only makes up the nonlinear characteristics and the memory effect of the power amplifier, but also gives play to the advantages of agility, stabilization and high efficiency of thefuzzy-neural network on the aspect of predistortion and power amplifier modeling.

Description

technical field [0001] The invention relates to a predistortion method for linearization of a power amplifier, in particular to a digital predistortion method based on a Hammerstein model of a fuzzy neural network (ANFIS, adaptive neuro-fuzzy inference system). Background technique [0002] In modern communication systems (WCDMA, CDMA200, and WIMAX, etc.), spectrum resources are increasingly scarce with the ever-increasing data traffic. In order to improve spectrum utilization, various linear modulation techniques (QPSK, QAM and OFDM, etc.) have been proposed. Although the new modulation method can effectively alleviate the contradiction between spectrum and high-speed data transmission, it is harmful to the radio frequency system in the communication system. New problems were raised. Since these digital modulation methods all belong to non-constant envelope modulation, the modulation method with higher efficiency often has a larger peak-to-average ratio (PAR, Peak-to-avera...

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Problems solved by technology

[0007] Technical solution: The power amplifier predistortion method based on the Hammerstein model of the fuzzy neural network of the present invention combines the advantages of the Voltera series and the neural network, avoids its disadvantages, and solves the large amount of calculation of the existing predistortion scheme. The adaptation algorithm is not easy to converge, and it is more complicated to implement. Under the conditions of high bandwidth and peak-to-average signal ratio, it is difficult to compensate the complex memory effect of the power amplifier and other issues.

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