Improvements and analysis of nonlinear parallel behavioral models

Abstract

This article performs an analysis of current limitations regarding the extraction of parallel behavioral models to reproduce the power amplifier (PA) nonlinear behavior and its dynamics. To overcome these limitations, a general preprocessing block that clearly improves the identification capabilities shown by classical parallel structures is proposed. It follows the principle of separating both static and dynamic nonlinear behavior of the PA to obtain a better identification performance. A comparison with common parallel configurations using linear estimation is performed, to highlight the benefits of using the preprocessing structure. Furthermore, a new nonlinear parallel structure using sub-band filtering techniques is also proposed. For the models extraction and comparison, four types of noise-free simulated data presenting different levels of nonlinearities and memory, as well as a measured signal obtained from a laboratory amplifier have been considered.

Innovative Theoretical Approaches Used for RF Power Amplifiers in Modern HDTV Systems

“…Therefore, it is possible to establish some relationships between the signal bandwidth with the second and higher-order distortions for a specified fundamental frequency for the system. In addition, it is possible to detect the fundamental zones that corresponding to odd or even distortions, and some of them can be removed using filters [17,19,23].…”

“…If linearization is used, many methods can be applied. The most frequently used ones are feed forward and pre-distortion with pros and cons listed in our initial references [16,17,19,20,23].…”

“…Operating the amplifier in significant back-off can avoid some of these undesirable distortions, but the efficiency of the amplifier will be very poor. A powerful way to decrease distortion in the output signal is to use linearization techniques, thus operating the PA in a more efficient way [16][17][18]. Some initial research for the AM/AM analysis can be obtained, by imposing that the transfer function of the output voltage of the power amplifier had third-order nonlinear dependence with respect to the input signal.…”

Evolution of Black-Box Models Based on Volterra Series

“…The first branch is a nonlinear static block, and the next branches are reduced Volterra series, each one estimated at a different subsampling rate (PssVS). This model and its estimation method are fully explained in [5], and it is shown in Figure 7.…”

“…Some models were recently reported: (i) the pruned Volterra series (rVS1) [3], (ii) the pruned Volterra series (rVS2) [4], (iii) a parallel cascade model (PCM) composed of a static nonlinearity and a reduced Volterra model (PNLrVS) [5], (iv) a parallel cascade subsampled reduced Volterra series with the first branch composed of a static nonlinearity and a rVS model and other branches being rVS models, all with the same memory depth (PssVS), as detailed in [5].…”