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

Maximum Power Transfer01:16

Maximum Power Transfer

330
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
330
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

158
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
158
The Maximum Power Transfer Theorem01:20

The Maximum Power Transfer Theorem

702
Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
The load connected draws the current, and the circuit delivers the power to the load. The alternating current flowing through the load is determined using the rectangular form of voltages, currents, network impedance, and load impedance. The average power delivered to the load is obtained from the product of the square of current and load resistance.
702
The Power Superposition Principle01:19

The Power Superposition Principle

198
Consider a circuit with two sinusoidal voltage sources. Each one influences the circuit independently, and the superposition principle helps us understand the combined effect by adding up the responses from each source.
198
Power System Distribution01:25

Power System Distribution

295
Power system distribution involves delivering electrical energy from power plants to consumers through a network of transmission and distribution systems. The process begins at power plants, where energy from coal, gas, nuclear, water, and wind is converted into electrical energy. These plants use three-phase generators, typically rated between 50 to 1300 MVA, with terminal voltages ranging from a few kV to 20 kV, depending on the size and age of the units.
The transmission system is designed...
295
Control of Power Flow01:30

Control of Power Flow

302
There are several methods to control power flow in power systems:
302

You might also read

Related Articles

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

Sort by
Same author

EcoImpact: energy conservation using data-driven model predictive control and interpretable machine learning in the buildings sector.

Scientific reports·2026
Same author

PHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning.

PloS one·2024
Same author

An Innovative Clustering Hierarchical Protocol for Data Collection from Remote Wireless Sensor Networks Based Internet of Things Applications.

Sensors (Basel, Switzerland)·2023
Same author

Efficient Approach for Anomaly Detection in IoT Using System Calls.

Sensors (Basel, Switzerland)·2023
Same author

Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET.

Sensors (Basel, Switzerland)·2022
Same author

Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey.

Sensors (Basel, Switzerland)·2022
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.1K

Cooperative Power-Domain NOMA Systems: An Overview.

Mujtaba Ghous1,2, Ahmad Kamal Hassan2, Ziaul Haq Abbas2

  • 1Telecommunication and Networking Research Lab, GIK Institute of Engineering Sciences and Technology, Topi 23640, Pakistan.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

Non-orthogonal multiple access (NOMA) enhances wireless network efficiency by overcoming interference, crucial for 5G and beyond. Cooperative NOMA systems improve quality of service and spectral efficiency for advanced applications.

Keywords:
antenna diversitycooperative communicationequaliseroutage probabilitypower-domain NOMAprecodersuccessive interference cancellation

More Related Videos

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

10.0K
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

3.2K

Related Experiment Videos

Last Updated: Aug 16, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
05:57

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

Published on: April 1, 2020

8.1K
Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

10.0K
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

3.2K

Area of Science:

  • Wireless communication networks
  • Signal processing
  • Telecommunications engineering

Background:

  • Interference is a major obstacle to deploying effective wireless applications in 5G and beyond networks.
  • Existing protocols and standards struggle to meet the Quality of Service (QoS) demands for many 5G applications due to persistent interference.
  • The growing need for higher data rates and multimedia content necessitates advanced solutions like Non-Orthogonal Multiple Access (NOMA).

Purpose of the Study:

  • To explore cooperative Non-Orthogonal Multiple Access (NOMA) systems as a solution to interference in wireless networks.
  • To provide an overview of power-domain NOMA-based cooperative communication strategies.
  • To identify future research directions in cooperative NOMA systems.

Main Methods:

  • Discussion of cooperative NOMA systems utilizing decode-and-forward and amplify-and-forward protocols.
  • Review of NOMA integration with technologies like massive MIMO, beamforming, and space-time coding to enhance performance.
  • Analysis of power-domain NOMA for improved spectral efficiency and QoS.

Main Results:

  • NOMA, particularly when combined with techniques like successive interference cancellation and superposition coding, significantly enhances spectral efficiency.
  • Cooperative NOMA systems demonstrate improved performance when integrated with antenna diversity and advanced wireless technologies.
  • The study highlights the effectiveness of decode-and-forward and amplify-and-forward protocols within cooperative NOMA frameworks.

Conclusions:

  • Cooperative NOMA offers a promising approach to mitigate interference and enhance QoS in 5G and beyond wireless networks.
  • Integrating NOMA with technologies such as massive MIMO and beamforming is key to unlocking its full potential.
  • Further research into cooperative NOMA systems is essential for future advancements in wireless communication.