2D quasi exact inverse of PA model in digital predistorter for concurrent dual-band system

Abstract

This paper reports a new direct learning (DL) technique using 2D quasi exact inverse (2D-QEI) of a power amplifier (PA) model for linearizing concurrent dual band PA. In contrast to indirect learning (IL) architecture, where the coefficients are extracted by swapping the input and output signals in any PA model, a QEI of a PA model can be used in the digital predistorter (DPD). A 2D memory polynomial (2D-MP) is used in both the cases to compare the performance. The evaluation of the model’s performance is conducted on an application close to real base station using a field programmable gate array (FPGA) and two radio transceivers. A 10W PA is excited with two wideband code division multiple access (WCDMA) signals of 3.84MHz bandwidth which are 310 MHz apart. The measurement results demonstrate that in the presence of additive noise, there is a noticeable improvement in terms of normalized mean square error (NMSE) and adjacent channel power ratio (ACPR) when using the QEI model for DPD. This improvement is achieved in a single step with no iteration as in practical DPD systems.