Magnitude Scaling-Based Behavioral Model for Power Amplifiers With Dynamic Power Transmission

In this letter, we present a magnitude scaling-based power scalable model for dynamic power transmission. Benefiting from the regularity of power amplifier’s (PA’s) behavior at different power levels, the proposed power adaptive model only needs to update the model parameters in the low magnitude during digital predistortion (DPD). A power factor is applied to scale the magnitude of the input signal, and a piecewise model is used to realize the segmented extraction of the parameters. Experiments carried out on a gallium nitride (GaN) PA show that the proposed method can reduce the model coefficients needed update by at least 50% with the comparable linearization performance.