An accurate digital baseband predistorter design for linearization of RF power amplifiers by a genetic algorithm based Hammerstein structure

Belabad, Ahmad Rahati; Sharifian, Saeed; Motamedi, Seyed Ahmad

doi:10.1007/s10470-018-1173-x

Cited by 15 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of nonlinear systems, the Hammerstein model is the most common block-oriented model. This model can well characterize the process properties and represent a large class of nonlinear processes, for instance, neutralization process [1], heat exchanger [2], power system [3], distillation towers [4,5], drying processes [6], and continuous stirred tank reactors [7,8].…”

Section: Introductionmentioning

confidence: 99%

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Zhu

et al. 2022

Asian Journal of Control

View full text Add to dashboard Cite

A novel parameter learning scheme using multi-signal processing is developed that aims at estimating parameters of the Hammerstein nonlinear model with output disturbance in this paper. The Hammerstein nonlinear model consists of a static nonlinear block and a dynamic linear block, and the multi-signals are devised to estimate separately the nonlinear block parameters and the linear block parameters; the parameter estimation procedure is greatly simplified. Firstly, in view of the input-output data of separable signals, the linear block parameters are computed through correlation analysis method, thereby the influence of output noise is effectively handled. In addition, model error probability density function technology is employed to estimate the nonlinear block parameters with the help of measurable input-output data of random signals, which not only controls the space state distribution of model error but also makes error distribution tends to normal distribution. The simulation results demonstrate that the developed approach obtains high learning accuracy and small modeling error, which verifies the effectiveness of the developed approach.

show abstract

Section: Introductionmentioning

confidence: 99%

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Zhu

et al. 2022

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…Models derived from the Volterra Series estimation have been proven to have appropriate accuracy in modeling stages for nonlinear systems. Alternative techniques such as Wiener, Hammerstein, and serial or parallel configurations consider the nonlinear (NL) and linear time invariant (LTI) stages as a suitable strategy in the modeling of nonlinear devices [7,8]. Additionally, memory polynomial model (MPM) and the generalized memory polynomial (GMP) model are related works that make important approximations in this area.…”

Section: Introductionmentioning

confidence: 99%

“…A novel output generalized memory polynomial (OGMP) behavioral model based on the previous output signal for linearization of PA was proposed in [9]. Conventional MP or GMP models use polynomials of the previous input signal to characterize memory effects [8]. The behavioral models for PAs devices driven with single and multiband signals have been designed to mimic the nonlinear regimes and to provide a useful approximation of its nonlinear response under several stimuli.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Linearization Method for Highly RF-PA Nonlinear Behavior Based on the Compression Region Identification

et al. 2021

View full text Add to dashboard Cite

In this paper, we present an adaptive modeling and linearization algorithm using the weighted memory polynomial model (W-MPM) implemented in a chain involving the indirect learning approach (ILA) as a linearization technique. The main aim of this paper is to offer an alternative to correcting the undesirable effect of spectral regrowth based on modeling and linearization stages, where the 1-dB compression point (P1dB) of a nonlinear device caused by memory effects within a short time is considered. The obtained accuracy is tested for a highly nonlinear behavior power amplifier (PA) properly measured using a field-programmable gate array (FPGA) system. The adaptive modeling stage shows, for the two PAs under test, performances with accuracies of −32.72 dB normalized mean square error (NMSE) using the memory polynomial model (MPM) compared with −38.03 dB NMSE using the W-MPM for the (i) 10 W gallium nitride (GaN) high-electron-mobility transistor (HEMT) radio frequency power amplifier (RF-PA) and of −44.34 dB NMSE based on the MPM and −44.90 dB NMSE using the W-MPM for (ii) a ZHL-42W+ at 2000 MHz. The modeling stage and algorithm are suitably implemented in an FPGA testbed. Furthermore, the methodology for measuring the RF-PA under test is discussed. The whole algorithm is able to adapt both stages due to the flexibility of the W-MPM model. The results prove that the W-MPM requires less coefficients compared with a static model. The error vector magnitude (EVM) is estimated for both the static and adaptive schemes, obtaining a considerable reduction in the transmitter chain. The development of an adaptive stage such as the W-MPM is ideal for digital predistortion (DPD) systems where the devices under test vary their electrical characteristics due to use or aging degradation.

show abstract

Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

Zhu

Cao

2022

Circuits Syst Signal Process

View full text Add to dashboard Cite

An accurate digital baseband predistorter design for linearization of RF power amplifiers by a genetic algorithm based Hammerstein structure

Cited by 15 publications

References 28 publications

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

A Weighted Linearization Method for Highly RF-PA Nonlinear Behavior Based on the Compression Region Identification

Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

Contact Info

Product

Resources

About