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

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

471
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
471
Classification of Signals01:30

Classification of Signals

1.3K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.3K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

640
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
640
Generating Electromagnetic Radiations01:10

Generating Electromagnetic Radiations

6.7K
The German physicist Heinrich Hertz (1857–1894) was the first to generate and detect certain types of electromagnetic waves in the laboratory. Starting in 1887, he performed a series of experiments that confirmed the existence of electromagnetic waves and verified that they travel at the speed of light. Hertz used an alternating-current RLC (resistor-inductor-capacitor) circuit that resonated at a known frequency and connected it to a loop of wire. High voltages induced across the gap in...
6.7K
Signal and System01:26

Signal and System

1.6K
A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
1.6K
Even and Odd Signals01:17

Even and Odd Signals

2.1K
An even signal, whether in continuous-time or discrete-time, is defined by its symmetry with its time-reversed version. Mathematically, this is represented as
2.1K

You might also read

Related Articles

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

Sort by
Same author

State estimation of multi-sensor systems based on error-state Kalman.

PloS one·2025
Same author

BAG3: a new therapeutic target of human cancers?

Histology and histopathology·2012
Same author

Z-Selectivity in olefin metathesis with chelated Ru catalysts: computational studies of mechanism and selectivity.

Journal of the American Chemical Society·2012
Same author

Complications after pancreaticoduodenectomy for pancreatic cancer: a retrospective study.

International surgery·2012
Same author

[Inhibitory effect of valproic acid on xenografted Kasumi-1 tumor growth in nude mouse and its mechanism].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2012
Same author

[Foix syndrome secondary to chemotherapy of acute nonlymphocytic leukemia: a case report and review of the literature].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2012
Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026
Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026
Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026
Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026
Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026
Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jan 15, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

11.3K

Conditional generative adversarial network technology for OFDM system receiver signal detection.

Yang Liu1, Peng Liu1, Yu Shi1

  • 1Department of Communication Electronic Countermeasure, Aviation University of Air Force, Changchun, China.

Plos One
|October 14, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced signal detection model for orthogonal frequency division multiplexing (OFDM) systems, significantly improving accuracy in complex wireless environments. The new model offers higher detection precision and faster processing, outperforming existing methods.

More Related Videos

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K

Related Experiment Videos

Last Updated: Jan 15, 2026

Quasi-light Storage for Optical Data Packets
07:45

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

11.3K
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

8.2K

Area of Science:

  • Wireless Communications
  • Signal Processing
  • Artificial Intelligence

Background:

  • Traditional orthogonal frequency division multiplexing (OFDM) systems exhibit limited detection accuracy in complex wireless channels.
  • Existing signal detection models struggle with robustness and efficiency in diverse environmental conditions.

Purpose of the Study:

  • To develop an optimized signal detection model for multiple-input multiple-output (MIMO) OFDM systems.
  • To enhance detection accuracy and computational efficiency in complex wireless environments.

Main Methods:

  • Utilized conditional generative adversarial networks (CGANs) to construct an initial single-input single-output (SISO) OFDM signal detection model.
  • Integrated deep complex neural networks (DCNNs) and quadratic concatenation of conditional information matrices to optimize the CGAN structure.
  • Proposed a novel MIMO-OFDM signal detection model.

Main Results:

  • Achieved a mean square error of channel detection as low as 0.2.
  • Demonstrated the lowest channel equalization error (1.23%) compared to advanced models.
  • In urban, suburban, and indoor environments, the model achieved a 98.72% signal reception success rate and 96.45% detection accuracy with an average detection time of 11.62ms.

Conclusions:

  • The proposed MIMO-OFDM signal detection model offers superior precision, computational efficiency, and robustness in complex wireless environments.
  • The model shows significant advantages over existing methods, providing a promising solution for reliable OFDM signal detection.
  • High adaptability and application prospects in challenging communication scenarios.