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Related Experiment Videos

A V2X communication resource allocation method based on graph neural networks and deep reinforcement learning.

Wenhong Yu1, Xinran Yang2, Shuo Yu2

  • 1School of Information Engineering, Dalian university, 116622, Dalian, China. yuwenhong@s.dlu.edu.cn.

Scientific Reports
|May 21, 2026
PubMed
Summary

This study introduces a novel method using graph neural networks and reinforcement learning to optimize spectrum and power allocation in vehicle-to-vehicle (V2V) communications. The approach enhances safety message transmission success rates and system capacity in the Internet of Vehicles (IoV).

Keywords:
Graph neural network(GNN)Reinforcement learning(RL)Resource allocation

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Area of Science:

  • Wireless Communication
  • Artificial Intelligence
  • Network Optimization

Background:

  • High-frequency vehicle-to-vehicle (V2V) communication in the Internet of Vehicles (IoV) faces spectrum collision and capacity limitations.
  • Safety information transmission in V2V requires high reliability.

Purpose of the Study:

  • To propose an integrated spectrum and power allocation method for V2V communication.
  • To enhance transmission success rates and system capacity while minimizing interference.

Main Methods:

  • Integration of Graph Neural Networks (GNNs) and Dueling Double Deep-Q Network (D3QN) reinforcement learning.
  • Graph construction representing V2V links and interference relationships.
  • GNN for feature extraction and D3QN for optimizing spectrum and power allocation.

Main Results:

  • Optimized spectrum and power allocation for V2V links.
  • Improved information transmission success rate for V2V safety messages.
  • Reduced interference to vehicle-to-infrastructure (V2I) links and increased V2I sum capacity.

Conclusions:

  • The proposed GNN-D3QN method effectively addresses spectrum collision and capacity issues in IoV.
  • The method enhances the reliability of safety-critical V2V communications.
  • Significant improvements in both V2V successful transmission rates and V2I system capacity were demonstrated.