Power Amplifier Modeling with Memory Polynomial Model

This article explains how to implement power amplifier modeling using the memory polynomial model in MATLAB. This modeling approach enables better understanding of power amplifier working principles and performance characteristics. To achieve this objective, we will elaborate on this modeling method from both theoretical and practical perspectives, providing practical implementation examples. The theoretical section will cover fundamental principles of polynomial models, design methodology of memory polynomial models, and comparisons with other modeling approaches. The practical implementation section will demonstrate how to code this model in MATLAB and validate its effectiveness through experimental verification. By studying this article, readers will gain comprehensive understanding of this modeling technique and be able to apply it to real-world engineering applications.

Key implementation aspects include: using MATLAB's matrix operations for efficient polynomial coefficient calculation, implementing memory effects through time-delay tap structures, and employing least-squares estimation for parameter extraction. The code structure typically involves input signal preprocessing, memory polynomial basis function generation, and model parameter optimization using built-in MATLAB functions like lsqnonlin or pinv for pseudo-inverse calculations.

The algorithm core revolves around representing the power amplifier's nonlinear behavior with memory using a Volterra series approximation, where the memory polynomial model serves as a simplified yet effective implementation. Practical code implementation would include functions for generating the regression matrix containing delayed input terms and their nonlinear combinations, followed by coefficient estimation routines that handle the computational optimization aspects.