A filterbank-based parallel hammerstein predistorter for high power amplifier linearization

High power amplifiers (HPAs) used in wideband systems exhibit evident memory effects besides nonlinearities, which significantly degrade the system performance. Asymmetries between lower and upper intermodulation products and spectral regrowth have been attributed to HPAs' memory effects, which limit the predistorter's linearization performance. In this paper, we propose a filterbank-based parallel Hammerstein predistorter, which is constructed using the indirect learning architecture. With the filterbank employed, the HPA with memory effects is linearized in the lower and upper frequency subbands individually. Simulation results show that the proposed predistorter is effective in linearizing HPAs with memory effects. When the HPA exhibits asymmetric spectral regrowth, compared with a memory polynomial predistorter, the proposed predistorter performs better in terms of spectral regrowth suppression and inband distortion reduction.