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Related Experiment Video

Updated: Dec 23, 2025

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

Published on: September 8, 2023

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Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks.

Hyebin Park1, Yujin Lim1

  • 1Department of IT engineering, Sookmyung Women's University, Seoul 04310, Korea.

Sensors (Basel, Switzerland)
|April 25, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a reinforcement learning technique for energy optimization in fifth-generation vehicular social networks (VSNs). The proposed method enhances energy efficiency and data rates by intelligently managing device-to-device communication interference.

Keywords:
5GD2D communicationmode selectionpower controlvehicle-to-vehicle communicationvehicular social network

Related Experiment Videos

Last Updated: Dec 23, 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

999

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Connected vehicles generate significant data traffic, necessitating efficient communication strategies in vehicular social networks (VSNs).
  • Device-to-device (D2D) communication offers reduced energy consumption and increased system capacity by reusing cellular resources.
  • D2D communication faces challenges from interference, requiring effective management techniques like mode selection and power control.

Purpose of the Study:

  • To propose a novel reinforcement learning (RL) technique for optimizing energy consumption in fifth-generation (5G) VSNs.
  • To enhance the performance of D2D communication by addressing interference through intelligent mode selection and power control.

Main Methods:

  • Utilized a hybrid RL approach combining centralized Q-learning for system-level decisions and distributed Q-learning for vehicle-level control.
  • Developed an algorithm to maximize system energy efficiency by dynamically adjusting the minimum signal-to-interference plus noise ratio (SINR) to ensure a target outage probability.

Main Results:

  • The proposed RL algorithm demonstrated superior performance compared to existing mode-selection and power-control algorithms.
  • Achieved significant improvements in system energy efficiency.
  • Showcased enhanced achievable data rates for connected vehicles.

Conclusions:

  • The developed RL technique effectively optimizes energy efficiency in 5G VSNs.
  • The approach successfully balances interference management with communication performance, leading to better data rates.
  • This method provides a viable solution for the growing demands of connected vehicle communication.