Obtaining the Preinverse of a Power Amplifier Using Iterative Learning Control

Telecommunication networks make extensive use of power amplifiers (PAs) 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 preinverse of the PA by the estimation of a nonlinear model. However, the extraction of this nonlinear model is involved and requires advanced system identification techniques. The plant inversion iterative learning control (ILC) algorithm is used here in combination with the best linear approximation to investigate whether the nonlinear modeling step can be simplified. This paper introduces the ILC framework for the preinverse estimation and predistortion of PAs. The ILC algorithm is used to obtain a high quality predistorted input for the PA under study without requiring a nonlinear model of the PA. In a second step, a nonlinear preinverse model of the amplifier is obtained. Both the nonlinear and memory effects of a PA 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 PA and a measurement example using the Chalmers RF WebLab measurement setup.

Publication year:2017

Keywords:Behavioral modeling, digital predistorter, iterative learning control (ILC), power amplifier (PA) linearization, RF PAs

CSS-citation score:1

Authors:International

Accessibility:Open