A Novel Digital Predistortion Coefficients Prediction Technique for Dynamic PA Nonlinearities Using Artificial Neural Networks

Abstract

This article presents a novel artificial neural network (ANN)-based digital predistortion (DPD) coefficients prediction (ANN-DPDCP) technique for dynamic nonlinearities induced by varying input power levels of power amplifiers (PAs). Conventional DPD techniques face challenges in mitigating dynamic nonlinearities efficiently. By modeling and predicting variations of conventional Volterra-based DPD coefficients using ANNs, the ANN-DPDCP technique rapidly provides appropriate DPD coefficients based on the target input power level. Benefiting from its concise training dataset and fitting capability, the ANN-DPDCP technique requires limited storage resources and derives DPD coefficients at arbitrary input power levels with negligible delay and comparable linearization performance. Experiments on a Ka-band PA driven by 100- and 400-MHz signals with a 12-dBm input power range illustrate storage resource reductions of 99.54% for 400 MHz and 99.81% for 100 MHz.