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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

157
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
157
Linear time-invariant Systems01:23

Linear time-invariant Systems

641
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
641
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

515
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
515
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

190
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,...
190
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

1.6K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
1.6K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

499
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...
499

You might also read

Related Articles

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

Sort by
Same author

Beyond REM: A New Approach to the Use of Image Classifiers for the Management of 6G Networks.

Sensors (Basel, Switzerland)·2023
Same author

Active Learning Methodology for Expert-Assisted Anomaly Detection in Mobile Communications.

Sensors (Basel, Switzerland)·2023
Same author

Victim Detection and Localization in Emergencies.

Sensors (Basel, Switzerland)·2022
Same author

WiFi FTM and UWB Characterization for Localization in Construction Sites.

Sensors (Basel, Switzerland)·2022
Same author

Dynamic Packet Duplication for Industrial URLLC.

Sensors (Basel, Switzerland)·2022
Same author

Location-Aware Node Management Solution for Multi-Radio Dual Connectivity Scenarios.

Sensors (Basel, Switzerland)·2021

Related Experiment Video

Updated: Nov 10, 2025

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

5.8K

A Multivariate Time-Series Based Approach for Quality Modeling in Wireless Networks.

Leonardo Aguayo1, Sergio Fortes2, Carlos Baena2

  • 1Departamento de Engenharia Elétrica, Universidade de Brasília, Campus Universitário Darcy Ribeiro, Brasília-DF 70910-900, Brazil.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

This study introduces a novel method for estimating wireless network Key Quality Indicators (KQIs) using adaptive filtering and clustering. The technique is designed for 5G/6G systems, offering real-time KQI monitoring in dynamic environments.

Keywords:
KQIQoEmachine-learningmobile networksmodeling

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.3K

Related Experiment Videos

Last Updated: Nov 10, 2025

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

5.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.3K

Area of Science:

  • Telecommunications Engineering
  • Network Performance Analysis
  • Machine Learning Applications

Background:

  • Accurate estimation of Key Quality Indicators (KQIs) is crucial for managing modern wireless networks.
  • Existing methods may struggle with the dynamic and non-stationary nature of advanced wireless environments like 5G and 6G.
  • The need for efficient, online KQI estimation methods is increasing with network complexity.

Purpose of the Study:

  • To develop and evaluate a novel method for estimating KQIs from node measurements in wireless networks.
  • To design a framework adaptable to 5G and 6G systems, capable of handling non-stationary conditions.
  • To demonstrate the feasibility of the proposed method using real-world network data.

Main Methods:

  • Utilized multivariate adaptive filtering and a clustering algorithm for KQI estimation.
  • Generated KQI time-series data suitable for network management systems.
  • Implemented a framework designed for fast, online training and flexibility.

Main Results:

  • The proposed method successfully estimated KQIs from live heterogeneous network data.
  • Initial results demonstrated the method's effectiveness compared to traditional linear regression techniques.
  • The framework showed adaptability to non-stationary network conditions.

Conclusions:

  • The presented method offers a viable approach for real-time KQI estimation in advanced wireless networks.
  • The framework's design supports application in future 5G and 6G systems.
  • Further research can refine the algorithms for enhanced performance and broader applicability.