Power Amplifier Nonlinear Characteristics and Predistortion Modeling Program

This program addresses the 2013 Mathematical Modeling Contest Problem B, focusing on power amplifier nonlinear characteristics and predistortion modeling. The implementation provides direct testing capabilities with multiple modeling approaches including Taylor series expansion models, polynomial approximation models, and neural network-based models. Through this program, researchers can systematically investigate power amplifier nonlinear behavior using different mathematical frameworks and develop corresponding predistortion compensation strategies. The code structure allows users to compare model performance through quantitative metrics like NMSE (Normalized Mean Square Error) and ACPR (Adjacent Channel Power Ratio). Key implementation features include: - Taylor series coefficients calculation for memoryless nonlinearity modeling - Polynomial fitting algorithms for behavioral modeling - Neural network training modules using backpropagation for adaptive predistortion - Signal processing pipelines for input-output characterization - Visualization tools for AM-AM and AM-PM distortion plots The program serves as a comprehensive research tool for understanding power amplifier operating principles and optimizing performance through predistortion techniques. By testing and comparing different models, researchers can identify the most suitable approach for their specific amplifier characteristics and enhance their study outcomes. This implementation provides both educational value for learning amplifier nonlinearities and practical utility for developing actual predistortion systems.