MATLAB Implementation of Polynomial-Based Power Amplifier Predistortion with Memory Effects

This MATLAB implementation demonstrates a polynomial-based predistortion algorithm for power amplifiers that accounts for memory effects. The algorithm employs polynomial modeling of input signals to precisely compensate for power amplifier nonlinearities when excited. Compared to conventional linear predistortion methods, this polynomial-based approach more accurately models the amplifier's nonlinear response, resulting in improved signal-to-noise ratio and reduced bit error rates. The code incorporates memory consideration by enabling the predistorter to adjust based on historical input signals. This is implemented through a recursive least squares (RLS) algorithm that dynamically updates polynomial coefficients. The RLS adaptation mechanism continuously optimizes coefficients using real-time error feedback, allowing the system to track signal variations and enhance predistortion performance. Key functions include polynomial basis generation, coefficient initialization, and adaptive filtering operations with forgetting factor control. In summary, this implementation provides a memory-aware polynomial predistortion solution that accurately predicts power amplifier nonlinear behavior. The algorithm's adaptive nature ensures robust performance across varying signal conditions, delivering superior linearization outcomes through systematic coefficient optimization and memory effect compensation.