A Digital Predistortion for Concurrent Dual-Band Power Amplifier Linearization Based on Periodically Nonuniform Sampling Theory

Artikel i vetenskaplig tidskrift, 2022

In this article, we propose a novel technique of digital predistortion (DPD) for dual-band power amplifiers (PAs) based on the periodically nonuniform sampling (PNS) theory. In contrast to conventional dual-band DPD (2-D-DPD) models, the proposed periodically nonuniform sampled DPD (PNS-DPD) has only a single input, which can largely reduce the model complexity. We fold the two stimuli with aliasing and feed them to a simple single-band DPD model. The desired predistorted signals are reconstructed from aliased DPD output through the PNS theory. Compared with conventional multi-input models that include numerous intermodulation products of the input signals, the complexity of the proposed single-input PNS-DPD model is hugely decreased. The model coefficients of the proposed PNS-DPD can be easily extracted with conventional direct or indirect learning architecture. We experimentally evaluate the proposed DPD on a test bench and compare it with other DPD techniques in the literature. The implementation complexity can be reduced by over 30%, and the identification complexity is also largely reduced.