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

Control of Power Flow01:30

Control of Power Flow

272
There are several methods to control power flow in power systems:
272
Maximum Power Transfer01:16

Maximum Power Transfer

265
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...
265
Load-frequency control01:28

Load-frequency control

170
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
170
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

120
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.
120
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.4K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.4K
Generator Voltage Control01:21

Generator Voltage Control

162
Generator voltage control is crucial for maintaining the stable operation of synchronous generators and wind turbines. In older models, a DC generator driven by the rotor delivers DC power to the rotor's field winding, and the power is transferred through slip rings and brushes. In the latest models, static or brushless exciters are used. Static exciters rectify AC power from the generator terminals and then transfer the DC power directly to the rotor. Brushless exciters, on the other hand,...
162

You might also read

Related Articles

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

Sort by
Same author

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization.

Sensors (Basel, Switzerland)·2017
See all related articles

Related Experiment Video

Updated: Jul 13, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K

Grant-Free NOMA: A Low-Complexity Power Control through User Clustering.

Abdulkadir Celik1

  • 1Computer, Electrical, and Mathematical Sciences & Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary

This study introduces synchronous grant-free NOMA (GF-NOMA) frameworks for massive IoT connectivity. GF-NOMA improves spectral efficiency and network lifetime by integrating UE clustering and power control for efficient resource utilization.

Keywords:
Internet of Thingsclusteringgrant-freemachine-type communicationsmassive connectivitynon-orthogonal multiple accesspower domainresource allocationuser pairing

More Related Videos

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

450
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

590

Related Experiment Videos

Last Updated: Jul 13, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

450
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

590

Area of Science:

  • Wireless Communications
  • Network Engineering
  • Signal Processing

Background:

  • Non-orthogonal multiple access (NOMA) enhances spectral efficiency for massive connectivity.
  • Traditional NOMA requires grant-based operation, hindering massive machine-type communications.
  • Existing NOMA schemes face challenges with channel-state information and power control.

Purpose of the Study:

  • Propose synchronous grant-free NOMA (GF-NOMA) frameworks for massive machine-type communications.
  • Integrate user equipment (UE) clustering and low-complexity power control within GF-NOMA.
  • Facilitate power-reception disparity essential for power-domain NOMA.

Main Methods:

  • Develop single-level GF-NOMA (SGF-NOMA) with identical transmit power for all UEs.
  • Introduce multi-level GF-NOMA (MGF-NOMA) grouping UEs by signal strength into partitions with distinct power levels.
  • Implement dynamic UE clustering based on objectives (max-sum/max-min rate), UE count, and resource blocks (RBs).

Main Results:

  • GF-NOMA frameworks compute clusters in milliseconds for hundreds of UEs.
  • MGF-NOMA achieves 96-99% of the optimal max-sum rate; SGF-NOMA reaches 87% at similar power.
  • SGF-NOMA demonstrates superior energy consumption fairness and network lifetime due to equal UE power usage.

Conclusions:

  • GF-NOMA frameworks offer efficient solutions for massive connectivity in IoT.
  • MGF-NOMA balances performance with resource utilization, while SGF-NOMA prioritizes energy fairness.
  • The proposed GF-NOMA schemes significantly improve upon traditional NOMA limitations for machine-type communications.