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

Observational Learning01:12

Observational Learning

360
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
360
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

186
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
186
Reinforcement01:23

Reinforcement

410
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
410
Introduction to Learning01:18

Introduction to Learning

588
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
588
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

184
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
184
Transfer Function to State Space01:23

Transfer Function to State Space

442
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
442

You might also read

Related Articles

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

Sort by
Same author

Federated Learning and Data Mining-Based Botnet Attack Detection Framework for Internet of Things.

Sensors (Basel, Switzerland)·2026
Same author

Leveraging Large Language Models for Automating Outpatients' Message Classifications of Electronic Medical Records.

Healthcare (Basel, Switzerland)·2025
Same author

A Survey on Free-Space Optical Communication with RF Backup: Models, Simulations, Experience, Machine Learning, Challenges and Future Directions.

Sensors (Basel, Switzerland)·2025
Same author

A Novel Cooperative AI-Based Fall Risk Prediction Model for Older Adults.

Sensors (Basel, Switzerland)·2025
Same author

A Multimodal Pain Sentiment Analysis System Using Ensembled Deep Learning Approaches for IoT-Enabled Healthcare Framework.

Sensors (Basel, Switzerland)·2025
Same author

Facial Anti-Spoofing Using "Clue Maps".

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: Oct 3, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.5K

Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning.

Masoto Chiputa1, Minglong Zhang1, G G Md Nawaz Ali2

  • 1Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

Fifth Generation (5G) mobile networks face handoff challenges due to millimeter waves (mmWaves). A new scheme using jump Markov linear systems and deep reinforcement learning significantly reduces wasteful handoffs, improving 5G connectivity.

Keywords:
Fifth Generationdeep reinforcement learninghandoverjump Markov linear systemmillimeter bands

More Related Videos

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

874

Related Experiment Videos

Last Updated: Oct 3, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.5K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

874

Area of Science:

  • Wireless Communication
  • Artificial Intelligence

Background:

  • Fifth Generation (5G) networks utilize millimeter waves (mmWaves) for high-speed data transmission.
  • mmWave links are susceptible to dynamic environmental and user-induced blockages, leading to irregular cell patterns and problematic handoffs (HOs).

Purpose of the Study:

  • To propose and evaluate a novel handoff (HO) scheme for 5G mmWave networks.
  • To mitigate issues of early, late, or incorrect HOs and sustain network connectivity.

Main Methods:

  • Integration of a jump Markov linear system (JMLS) to model abrupt system dynamics.
  • Application of deep reinforcement learning (DRL) for learning complex, time-varying behaviors.
  • Development of a JMLS-DRL platform for predicting mmWave link deterioration and formulating intelligent HO policies, optimized via meta-training.

Main Results:

  • The proposed JMLS-DRL HO scheme demonstrates superior reliability in selecting target links compared to SINR and DRL-only schemes.
  • Achieved reduction in wasteful HOs to under 5% within 200 training episodes, significantly outperforming the DRL-based scheme.
  • Supported longer dwell times between HOs and higher sum rates by minimizing unnecessary HOs, reducing HO frequency by nearly half.

Conclusions:

  • The JMLS-DRL platform provides an intelligent and versatile solution for 5G mmWave handoff challenges.
  • This approach effectively addresses the time-varying and abrupt nature of mmWave link behavior.
  • The proposed scheme enhances 5G network performance by ensuring reliable connectivity and efficient resource utilization.