Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

135
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
135
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.3K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.3K
Active Filters01:25

Active Filters

926
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
926
Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

177
Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the...
177
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

125
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
125
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

350
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
350

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mirror Descent and Exponentiated Gradient Algorithms Using Trace-Form Entropies.

Entropy (Basel, Switzerland)·2025
Same author

Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization.

Sensors (Basel, Switzerland)·2021
Same author

A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters.

Sensors (Basel, Switzerland)·2021
Same author

Upgrading Behavioral Models for the Design of Digital Predistorters.

Sensors (Basel, Switzerland)·2021
Same author

Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison.

Entropy (Basel, Switzerland)·2020
Same author

Centroid-Based Clustering with <i>αβ</i>-Divergences.

Entropy (Basel, Switzerland)·2020

Related Experiment Video

Updated: Sep 13, 2025

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks
10:07

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

27.2K

A Sparse Bayesian Technique to Learn the Frequency-Domain Active Regressors in OFDM Wireless Systems.

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)
|July 30, 2025
PubMed
Summary

This study introduces a frequency domain Sparse Bayesian Learning (SBL) algorithm for modeling nonlinear distortion in power amplifiers (PAs) used in orthogonal frequency division multiplexing (OFDM) systems. The new method offers comparable accuracy to time domain approaches but with significantly improved efficiency for wider bandwidths.

Keywords:
OFDMVolterra seriesbehavioral modelingfrequency domainnonlinear model identificationpower amplifiersparse Bayesian learning

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.5K

Related Experiment Videos

Last Updated: Sep 13, 2025

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks
10:07

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Published on: December 2, 2015

27.2K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.5K

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Wireless Communications

Background:

  • Nonlinear behavioral modeling of power amplifiers (PAs) is crucial for wireless systems.
  • Most research has focused on time-domain (TD) modeling, with limited exploration in the frequency domain (FD).
  • Orthogonal frequency division multiplexing (OFDM) systems present an opportunity for FD modeling.

Purpose of the Study:

  • To develop and demonstrate a frequency domain Sparse Bayesian Learning (FD-SBL) algorithm for modeling nonlinear distortion in wireless OFDM systems.
  • To identify an efficient and accurate reduced set of regressors for PA behavioral models.
  • To enable prediction of nonlinear distortion for successive OFDM symbols.

Main Methods:

  • Proposed a novel FD-SBL algorithm for PA nonlinear distortion modeling.
  • Utilized SBL to identify active FD regressors and estimate PA model coefficients.
  • Applied estimated coefficients for predicting distortion in subsequent OFDM symbols.

Main Results:

  • Achieved a validation Normalized Mean Squared Error (NMSE) of -47 dB for a 30 MHz bandwidth signal, comparable to TD-SBL (-46.6 dB).
  • FD-SBL demonstrated superior performance for a 100 MHz bandwidth signal, yielding an NMSE of -38.6 dB.
  • TD-SBL encountered excessive processing time and numerical issues at 100 MHz bandwidth, rendering it impractical.

Conclusions:

  • The proposed FD-SBL algorithm provides an efficient and accurate method for nonlinear distortion modeling in OFDM systems.
  • FD-SBL overcomes the computational limitations of TD-SBL for high-bandwidth signals.
  • This approach is promising for enhancing the performance and efficiency of modern wireless communication systems.