Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Detection of Gross Error: The Q Test

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

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).

Linear Approximation in Frequency Domain

Linear Approximation in Frequency Domain

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....

You might also read

Related Articles

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

Sort by

Same author

Computational modeling of left ventricular flow using PC-CMR-derived four-dimensional wall motion.

International journal of engineering science·2026

Same author

Robust feature selection for cancer microarray data using a hybrid mRMR and Binary Lion Optimization Algorithm.

Scientific reports·2026

Same author

A Dynamic 3D Human Liver Sinusoid Model for Mechanistic Interrogation of Fontan-Associated Liver Disease.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Retraction Note: Enhancing blockchain transaction classification with ensemble learning approaches.

Scientific reports·2026

Same author

Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling.

Advanced healthcare materials·2026

Same author

Explainable deep learning for skin cancer detection using swish-activated convolutional networks.

Discover oncology·2026

Search research articles

Related Experiment Video

Updated: Feb 26, 2026

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Performance analysis over q-Weibull fading channels for symbol error probability evaluation using a tighter Gaussian

Sarbeswar Samal¹, Sujata Chakravarty¹, Tanmay Mukherjee²

¹Department of Computer Science and Engineering, Centurion University of Technology and Management, Jatni, Bhubaneswar, Odisha, 752050, India.

Scientific Reports

|February 24, 2026

Summary

This study introduces a novel approximation for the Gaussian Q-function to accurately calculate symbol error probability (SEP) in wireless systems. The new method offers a tighter fit across all signal-to-noise ratios (SNR) and provides analytical solutions for the q-Weibull fading channel.

Keywords:

q-Weibull distribution Exponential-type approximation Gaussian Q-function Performance evaluation Symbol error probability Wireless fading

Related Experiment Videos

Last Updated: Feb 26, 2026

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Area of Science:

Wireless Communications
Information Theory
Mathematical Analysis

Background:

Accurate evaluation of symbol error probability (SEP) is crucial for wireless system performance.
Existing approximations for the Gaussian Q-function may lack accuracy across the full signal-to-noise ratio (SNR) range.
The q-Weibull fading model, based on Tsallis' entropy, offers adaptive characteristics for modeling wireless channels.

Purpose of the Study:

To develop a tight and closed-form approximation for the Gaussian Q-function.
To derive an analytical solution for SEP over the q-Weibull fading channel.
To analyze performance metrics like Level Crossing Rate (LCR) and Average Fade Duration (AFD) over the q-Weibull model.

Main Methods:

Employing the Gauss-Legendre four-point rule to derive an exponential-type approximation for the Gaussian Q-function.
Applying the derived approximation to obtain analytical SEP solutions for the q-Weibull fading channel.
Investigating the impact of the entropic index (q) and shape parameter (λ) on SEP, LCR, and AFD.

Main Results:

The proposed exponential-type approximation demonstrates superior agreement compared to existing methods for SEP computation.
The approximation provides a tighter fit across low-to-high SNR ranges.
Analytical solutions for SEP, LCR, and AFD over the q-Weibull channel were successfully obtained, showing adaptive behavior with varying 'q'.

Conclusions:

The novel Gaussian Q-function approximation enhances SEP calculation accuracy in wireless fading environments.
The analytical solutions for the q-Weibull channel provide valuable insights into system performance under adaptive fading conditions.
This research offers a more accurate and flexible approach to modeling and analyzing wireless communication systems.