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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Upgrading Behavioral Models for the Design of Digital Predistorters.

Carlos Crespo-Cadenas1, María José Madero-Ayora1, Juan A Becerra1

  • 1Departamento de Teoría de la Señal y Comunicaciones, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092 Seville, Spain.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary

This study introduces a method to enhance power amplifier (PA) models for digital predistortion (DPD). The technique improves model accuracy by adding significant nonlinear and memory terms, boosting linearization performance.

Keywords:
Volterra seriesbehavioral modelingdigital predistortionnonlinear model identificationpower amplifier linearization

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Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Communications Engineering

Background:

  • Power amplifier (PA) behavioral models and digital predistortion (DPD) are often truncated due to computational constraints.
  • A priori pruned model structures may omit significant terms, limiting performance.

Purpose of the Study:

  • To present a strategy for upgrading incomplete PA models.
  • To improve the performance of DPD systems by enhancing model accuracy.

Main Methods:

  • A general procedure to augment model structures by incorporating significant nonlinear and memory depth terms.
  • Utilizing the sparse nature of the problem for successive search and addition of terms.
  • Investigating the approach on commercial class AB and class J PAs.

Main Results:

  • Demonstrated capability to augment incomplete PA models effectively.
  • Significant improvement in the linearization capabilities of DPDs was observed.
  • The method successfully identified and incorporated crucial nonlinear and memory terms.

Conclusions:

  • The proposed upgrading procedure enhances PA behavioral models.
  • This leads to superior linearization performance in DPD applications.
  • The strategy offers a viable solution for improving PA modeling with limited resources.