Method for improving predistortion performance of radio-frequency power amplifier

Abstract

The invention provides a method for improving predistortion performance of a radio-frequency power amplifier, which utilizes an orthogonal polynomial to replace an original polynomial model or an original Volterra series model to solve the problem of an unstable value when parameters are estimated. Besides, a generation mode of the orthogonal polynomial is given, and corresponding model parameter estimation algorithm is provided. Compared with the prior art, the method solves the problem of the unstable value when a predistortion linearization model of the radio-frequency power amplifier estimates the parameters, and the method has the advantages of being simple in structure, low in fixed-point achieving cost and the like.

Description

technical field [0001] The present invention relates to a method for improving the predistortion performance of a radio frequency power amplifier, in particular to a method for solving the numerical instability during predistortion parameter estimation by using orthogonal polynomials and ensuring the linearization of the predistortion of the radio frequency power amplifier, which belongs to wireless communication technology field. Background technique [0002] In a wireless communication system, the energy consumption of the wireless communication base station and its supporting equipment accounts for about 80% of the total energy consumption. Among them, the radio frequency power amplifier (RFPA) is one of the most important energy-consuming devices. Improving the power efficiency of RF power amplifiers is of great significance to the energy saving and environmental protection of wireless communication base stations. [0003] For traditional RF power amplifiers, their lin...

AI Technical Summary

Problems solved by technology

In the implementation of the predistortion linearization model parameter estimation algorithm, the matrix Φ H Φ inversion may bring numerical instability, which greatly affects the effect of parameter estimation and linearization

In addition, in the process of solving the least mean squares algorithm, although it is not necessary to H Φ inverse, but because the columns of the matrix Φ are highly correlated, there will be slow convergence speed or failure to converge to the optimal value during the parameter estimation process of the predistortion linearization model

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