Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers

Digital predistortion (DPD) is essential for improving the efficiency and linearity of power amplifiers (PAs), particularly in radio frequency communication systems. We propose a wavelet decomposition prediction (WDP) framework that better adapts to the highly nonlinear characteristics of PAs. In this framework, the input data are first decomposed using wavelet transformation, allowing for a more effective representation of nonlinear features. Next, a nonlinear modeling process is conducted on the PA to capture its distortion characteristics. Once the nonlinear model is trained, it is frozen to preserve its learned features. Based on this frozen nonlinear model, DPD is then applied to the PA to compensate for nonlinear distortions. Experimental results demonstrate the effectiveness of our proposed method, achieving the best ACPR and EVM performance on the OpenDPD dataset.