An augmented radial basis function neural network (ARBFNN) is proposed for modelling and linearising a wideband Doherty power amplifier (DPA) with strong memory effects and static nonlinearity. To evaluate the performance of the ARBFNN, a 51 dBm DPA and a 25 MHz mixed test signal were used in modelling and linearisation measurement. Compared with the memory polynomial (MP) model and the real-valued time-delay neural network (RVTDNN), the ARBFNN is highly effective, leading to 3 and 5 dB improvements in the normalised mean square error. More importantly, the ARBFNN predistorter represents a significant improvement over the RVTDNN and MP in the suppression of the out-of-band spectral regrowth. In addition, the ARBFNN has a similar linearisation capability as the generalised MP model, but has much better numerical stability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.