Digital Predistortion of Nonlinear RF Power Amplifier with Memory Effects

This project focuses on digital predistortion (DPD) techniques for nonlinear RF power amplifiers demonstrating memory effects. The implementation employs MATLAB code to simulate DPD-based linearization for class-AB nonlinear high-power amplifiers (HPAs) with memory characteristics. A memory polynomial predistorter architecture is implemented to accurately model both nonlinear distortion and memory effects, where the algorithm utilizes cross-terms between current and past input samples to capture dynamic nonlinear behavior.

The simulation workflow comprises three key phases: First, the code incorporates compensation algorithms for analog impairments in direct upconversion transmitters, including IQ imbalance and LO leakage correction. Second, the memory polynomial DPD design phase involves coefficient estimation through least-squares fitting, where the predistorter function is expressed as a sum of delayed nonlinear terms. Finally, system performance is evaluated using standard metrics: Error Vector Magnitude (EVM) calculations quantify modulation accuracy, Power Spectral Density (PSD) analyses assess spectral regrowth suppression, and Signal-to-Noise Ratio (SNR) measurements determine linearization effectiveness.

The simulation results demonstrate significant practical value for wireless communication systems. By implementing digital predistortion for nonlinear RF power amplifiers with memory effects, the technique substantially improves transmitted signal quality and spectral efficiency. The MATLAB implementation provides a comprehensive framework for evaluating DPD algorithms, making it particularly valuable for 5G and millimeter-wave communication systems where power amplifier linearization is critical.