An Adaptive Cascaded ILA- and DLA-Based Digital Predistorter for Linearizing an RF Power Amplifier

Abstract

This paper presents a novel adaptive digital predistortion (DPD) technique based on a cascade of an adaptive indirect learning architecture (ILA) and a static direct learning architecture (DLA) using a linear interpolation look-up-table (LILUT). The static LILUT-DLA-based DPD is designed to identify the inverse of a radio-frequency power amplifier (PA) model. The cascaded system of the DLA-based predistorter (PD) and PA is theoretically linear. However, in real-time applications, the PA characteristics change with time due to process, supply voltage, and temperature variations, making this cascaded system not strictly linear, which results in some residual nonlinear distortion at the PA output. This residual distortion is effectively compensated by an additional adaptive ILA-based PD using least mean squares or recursive least squares. Thanks to the incorporation of the static DLA, the proposed DPD approach is less sensitive to the PA output noise, ensuring a better preinverse of the PA and also requiring a smaller number of adaptive coefficients than either the adaptive stand-alone DLA- or ILA-based DPDs. The experimental results show that the proposed DPD technique effectively linearizes the PA, even if its characteristics change, and obtains better linearization performance than either the classical stand-alone DLA- or stand-alone ILA-based DPDs.