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

Coefficient of Correlation01:12

Coefficient of Correlation

6.3K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.3K
Correlation01:09

Correlation

12.4K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
12.4K
Correlations02:20

Correlations

33.8K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
33.8K
Correlation of Experimental Data01:23

Correlation of Experimental Data

268
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
268
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
2.6K
Correlation and Regression00:53

Correlation and Regression

1.6K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Experimental radiation shielding investigation of the holmium oxide effects on GTB glass system.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same author

Various gamma-ray energies attenuation features of boro-lead-telluride glass-ceramic: An experimental study.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026
Same author

Performance enhancement of high order Hahn polynomials using multithreading.

PloS one·2023
Same author

3D Object Recognition Using Fast Overlapped Block Processing Technique.

Sensors (Basel, Switzerland)·2022
Same author

Reliable Recurrence Algorithm for High-Order Krawtchouk Polynomials.

Entropy (Basel, Switzerland)·2021
Same author

On Computational Aspects of Krawtchouk Polynomials for High Orders.

Journal of imaging·2021
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
See all related articles

Related Experiment Video

Updated: Sep 3, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K

Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under

Alaa M Abdul-Hadi1, Marwah Abdulrazzaq Naser2, Muntadher Alsabah3

  • 1Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad, Iraq.

Peerj. Computer Science
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

Accurate channel state information (CSI) is crucial for massive multiple-input multiple-output (MIMO) systems. This study develops a new training sequence design to improve CSI estimation in frequency-division-duplex (FDD) massive-MIMO, especially under short coherence times.

Keywords:
Correlation modelFrequency-division-duplexMassive-MIMO

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells
14:12

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

5.5K

Related Experiment Videos

Last Updated: Sep 3, 2025

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells
14:12

Dual-Color Fluorescence Cross-Correlation Spectroscopy to Study Protein-Protein Interaction and Protein Dynamics in Live Cells

Published on: December 11, 2021

5.5K

Area of Science:

  • Wireless Communications
  • Signal Processing
  • Information Theory

Background:

  • Massive MIMO systems require accurate downlink (DL) channel state information (CSI) for optimal performance.
  • Frequency-division-duplex (FDD) massive MIMO faces challenges with low overhead CSI estimation due to short coherence times (CT).
  • Existing methods struggle to balance CSI accuracy and overhead in FDD massive MIMO under practical constraints.

Purpose of the Study:

  • To address the challenge of DL CSI estimation in FDD massive MIMO with short CT.
  • To propose a novel training sequence design that exploits physical channel correlation.
  • To analyze the impact of this design on achievable sum rate (ASR) and mean square error (MSE) of CSI estimation.

Main Methods:

  • Exploiting the statistical structure of massive MIMO channels via physical correlation models.
  • Designing training sequences based on eigenvectors of the transmit correlation matrix to reduce overhead.
  • Investigating the trade-offs between ASR maximization and MSE minimization for CSI estimation.
  • Analyzing the phenomenon of channel hardening in FDD massive MIMO systems.

Main Results:

  • The proposed training sequence design effectively reduces DL CSI estimation overhead.
  • Increasing correlation levels reduces CSI estimation MSE but does not necessarily increase ASR.
  • Significant loss in channel hardening is observed in high correlation scenarios.
  • Exploiting spatial correlation structure is essential for FDD massive MIMO with limited CT.

Conclusions:

  • The developed training sequence design offers a viable solution for accurate DL CSI estimation in FDD massive MIMO under short CT.
  • Understanding and exploiting channel correlation is critical for optimizing massive MIMO system performance.
  • Channel hardening effects need careful consideration in high correlation environments.