Robust behavioral modeling of dynamic nonlinearities using Gegenbauer polynomials with application to RF power amplifiers

Abstract

In this article, we propose a new set of basis functions based on Gegenbauer polynomials suitable for robust behavioral modeling of nonlinear dynamic systems. These polynomials can be optimized for maximum model identification stability under different input signal distributions. The efficiency and robustness of the proposed polynomial models are demonstrated and compared to the ones of previously published models. The obtained results revealed an exceptional numerical stability regardless of the input signal statistics, making the proposed new models suitable for multimode and broadband nonlinear wireless transmitters.

Behavioral modeling of nonlinear RF power amplifiers using ensemble SDBCC network

“…With the development of newer communication systems such as W-CDMA, wider PA bandwidths have become imperative and the memory effects of the PA are becoming increasingly important to performance. The most critical first step and also the key issue in analyzing a PA system and designing a linearizer is to model the PA nonlinearity accurately [4,5,17]. In recent years increasing attention has been given to behavioral modeling which is often used in PA nonlinearity modeling [16,20,28].…”

Orthonormal basis functions for memory predistorter in oversampled systems

“…The former degrades the error vector magnitude whereas the latter increases the adjacent channel interference [1,2]. Moreover, the nonlinearity changes over time, temperature, the average power of the input signal, and so forth [3,4]. To track and compensate for the time-varying nonlinearity, adaptive digital predistortion (DPD) technique is attractive as it provides a good compromise between implementation complexity and linearization performance [5].…”