Behavioral Modeling the Power Amplifier with Memory Effect Using the NARMA Model

摘要

Behavioral modeling is an important approach to access the characteristic of the power amplifier (PA) and to perform the numerical simulation. Polynomial model is the most popular tool to behaviorally model a PA which is always been considered to be a weak nonlinear system. When wideband signal applied, the frequency-dependence, namely, memory effect is inevitable. The polynomial always becomes very complicated to model the PA with deep memory effect. Nonlinear auto-regressive moving average (NARMA), which can be seen as a recursive polynomial, has few implementations in power amplifier behavioral modeling. As will be shown hereinafter, NARMA model demonstrates low complexity and fairly good accuracy to model an actual PA. To develop a robust and numerically stable identification algorithm, orthogonal polynomial is used to improve the numerical stability of the matrix inverse.