Digital Predistortion System with Wiener-Hammerstein Power Amplifier and Memory Polynomial Predistortion Model

This article examines key concepts of digital predistortion systems, power amplifier characteristics, and predistortion models, along with the implementation of adaptive algorithms. Digital predistortion (DPD) represents a signal processing technique that reduces signal distortion by preprocessing signals prior to transmission. Power amplifiers are electronic devices that amplify signals to appropriate levels for transmission to speakers or other output devices. Our study focuses on the Wiener-Hammerstein model, widely implemented in power amplifier design through cascaded linear and nonlinear blocks. The predistortion technique employs memory polynomial models, which can be implemented using basis function expansion with code structures like: x_predistorted = sum_{k=1}^K sum_{m=0}^M a_{km} * x(n-m) * |x(n-m)|^(k-1) where K represents nonlinear order and M denotes memory depth. Finally, we analyze adaptive algorithms that utilize feedback mechanisms to automatically adjust DPD parameters. The RLS-LMS hybrid algorithm combines Recursive Least Squares' fast convergence with Least Mean Squares' computational efficiency, implemented through weight update equations: w(n+1) = w(n) + mu * e(n) * x(n) % LMS component P(n) = (P(n-1) - gain*P(n-1)*x(n)*x'(n)*P(n-1)) / lambda % RLS component This hybrid approach simultaneously optimizes both signal processing efficiency and power amplifier design performance.