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

Classification of Signals01:30

Classification of Signals

702
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...
702
Classification of Systems-I01:26

Classification of Systems-I

266
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
266
Classification of Systems-II01:31

Classification of Systems-II

213
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
213
Aggregates Classification01:29

Aggregates Classification

366
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
366
Maximum Power Transfer01:16

Maximum Power Transfer

343
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...
343
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Performance Enhancement for B5G/6G Networks Based on Space Time Coding Schemes Assisted by Intelligent Reflecting Surfaces with Higher Modulation Orders.

Sensors (Basel, Switzerland)·2024
Same author

DGD-CNet: Denoising Gated Recurrent Unit with a Dropout-Based CSI Network for IRS-Aided Massive MIMO Systems.

Sensors (Basel, Switzerland)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

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

Related Experiment Video

Updated: Aug 22, 2025

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

637

Binary PSO with Classification Trees Algorithm for Enhancing Power Efficiency in 5G Networks.

Mayada Osama1,2, Salwa El Ramly2, Bassant Abdelhamid2

  • 1Electronics and Communications Department, Faculty of Engineering Science and Arts, Misr International University, Cairo 11828, Egypt.

Sensors (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces machine learning algorithms to optimize 5G small cell networks. By intelligently switching off redundant small cells (SCs) and managing interference, the proposed methods enhance power efficiency (PE) and network throughput.

Keywords:
5G HetNetsBPSOclassification trees (CTs)small cells (SCs)soft frequency reuse (SFR)

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

606
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Related Experiment Videos

Last Updated: Aug 22, 2025

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

637
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

606
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

Area of Science:

  • Telecommunications Engineering
  • Computer Science
  • Network Optimization

Background:

  • Dense deployment of small cells (SCs) in 5G heterogeneous networks (HetNets) increases connectivity but reduces power efficiency (PE) due to high power consumption and interference.
  • Maintaining Quality of Service (QoS) while improving network performance is a key challenge in 5G HetNets.

Purpose of the Study:

  • To propose novel machine learning (ML) algorithms for optimizing power efficiency (PE) in 5G small cell networks.
  • To minimize network power consumption and alleviate inter-cell interference.
  • To enhance overall system throughput and maintain Quality of Service (QoS).

Main Methods:

  • Implementation of a linearly increasing inertia weight-binary particle swarm optimization (IW-BPSO) algorithm for dynamic on/off switching of small cells (SCs).
  • Development of a soft frequency reuse (SFR) algorithm utilizing classification trees (CTs) to mitigate interference.
  • Evaluation of proposed algorithms against conventional methods for performance comparison.

Main Results:

  • The proposed IW-BPSO algorithm effectively minimizes power consumption by switching off redundant small cells (SCs).
  • The classification tree (CT)-based soft frequency reuse (SFR) algorithm successfully alleviates interference between SCs.
  • The combined approach significantly reduces network power consumption and interference, leading to improved total throughput and power efficiency (PE).

Conclusions:

  • The proposed IW-BPSO and SFR algorithms offer a superior solution for optimizing 5G heterogeneous networks (HetNets).
  • These methods effectively balance power consumption, interference management, and throughput, enhancing overall network performance.
  • The study demonstrates the potential of machine learning (ML) techniques in achieving efficient and high-performance 5G networks.