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

International audienceIn this paper, we propose a novel technique of digital predistortion (DPD) for dual-band power amplifiers (PA) based on periodically nonuniform sampling (PNS) theory. In contrast to conventional 2D-DPD models, the proposed PNS-DPD has only a single input which can largely reduce the model complexity. We fold the two stimuli with aliasing and feed it 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 which 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