Novel Adaptive Digital Predistortion Based on the Hybrid Indirect Learning Algorithm

Adaptive digital predistortion (DPD) is one of the most promising linearization technique, which leads to more efficient and cost-effective high power amplifier (HPA). In this paper, we propose a novel adaptive DPD based on the hybrid indirect learning (HIL) algorithm, which can not only remedy the effect caused by measurement noise in the feedback loop effectively, but also improve the convergence stability and reduce the overall cost of DPD implementation simultaneously. The effectiveness of this scheme in the presence of the measurement noise was confirmed through computer simulations.