Obtaining the Pre-Inverse of a Power Amplifier using Iterative Learning Control

TL;DR

This paper introduces an iterative learning control method to estimate the pre-inverse of a power amplifier, simplifying nonlinear modeling and improving predistortion without complex system identification.

Contribution

It presents a novel iterative learning control framework for power amplifier predistortion that bypasses the need for detailed nonlinear model extraction.

Findings

Successful simulation on Motorola LDMOS amplifier

Effective predistortion demonstrated in measurement setup

Convergence of the iterative learning control algorithm

Abstract

Telecommunication networks make extensive use of power amplifiers to broaden the coverage from transmitter to receiver. Achieving high power efficiency is challenging and comes at a price: the wanted linear performance is degraded due to nonlinear effects. To compensate for these nonlinear disturbances, existing techniques compute the pre-inverse of the power amplifier by estimation of a nonlinear model. However, the extraction of this nonlinear model is involved and requires advanced system identification techniques. We used the plant inversion iterative learning control algorithm to investigate whether the nonlinear modeling step can be simplified. This paper introduces the iterative learning control framework for the pre-inverse estimation and predistortion of power amplifiers. The iterative learning control algorithm is used to obtain a high quality predistorted input for the power amplifier under study without requiring a nonlinear model of the power amplifier. In a second step a nonlinear pre-inverse model of the amplifier is obtained. Both the nonlinear and memory effects of a power amplifier can be compensated by this approach. The convergence of the iterative approach, and the predistortion results are illustrated on a simulation of a Motorola LDMOS transistor based power amplifier and a measurement example using the Chalmers RF WebLab measurement setup.