In this paper a low complexity behavioral model based on dynamic nonlinearity reduction is proposed for the behavioral modeling of power amplifiers driven by LTE-advanced signals. This allows for a reduction in the number of model coefficients' without compromising its accuracy by independently minimizing the nonlinearity orders in each of the branches of the memory polynomial function. Experimental validation carried out on a class-J power amplifier driven by a 100-MHz LTE signal demonstrates the ability of the proposed model to accurately predict the amplifier's output. Compared to the conventional twin-nonlinear two-box model, the dynamic nonlinearity reduction based twin-nonlinear two-box model reduces the number coefficients of the memory polynomial function from 75 to 30 while degrading the model's NMSE by only 0.1dB.
- Behavioral modeling
- memory effects