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Related Experiment Video

Updated: Sep 9, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Dynamic Allocation of C-V2X Communication Resources Based on Graph Attention Network and Deep Reinforcement Learning.

Zhijuan Li1,2,3, Guohong Li1, Zhuofei Wu4

  • 1School of Computer and Big Data, Heilongjiang University, Harbin 150080, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

This study introduces a new AI framework to optimize resource allocation for vehicle communications. The GAT-A2C model enhances both traffic safety (V2V) and data services (V2N) in intelligent transport systems.

Keywords:
advantage actor–criticdynamic vehicular networksgraph attention networksreinforcement learningvehicle-to-vehicle

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Area of Science:

  • Wireless Communications
  • Artificial Intelligence
  • Intelligent Transport Systems

Background:

  • Vehicle-to-vehicle (V2V) and vehicle-to-network (V2N) communications are crucial for intelligent transport systems (ITSs).
  • Sharing spectrum resources for V2V (safety) and V2N (infotainment) presents significant resource allocation challenges in dynamic traffic environments.
  • Existing methods struggle to balance reliable V2V transmission with high-rate V2N services.

Purpose of the Study:

  • To propose a novel reinforcement learning (RL) framework for joint resource allocation in V2V and V2N communications.
  • To address the challenges of resource-constrained and dynamic vehicular network environments.
  • To optimize resource blocks and transmission power for improved communication performance.

Main Methods:

  • Developed a Graph Attention Network (GAT)-Advantage Actor-Critic (GAT-A2C) reinforcement learning framework.
  • Constructed a graph representing V2V links and interference relationships, with V2V links as nodes and interference as edges.
  • Utilized GAT to capture interference patterns and combined them with link characteristics for the RL environment state.
  • Employed the RL agent to jointly optimize resource blocks allocation and transmission power for V2V and V2N.

Main Results:

  • The GAT-A2C framework significantly improved V2N data rates.
  • The proposed method substantially increased V2V communication success ratios across various vehicle densities.
  • Demonstrated substantial improvements in both V2N rates and V2V communication success ratios.

Conclusions:

  • The GAT-A2C approach offers a promising solution for resource allocation in intelligent vehicular networks.
  • The framework exhibits strong scalability for future large-scale, dynamic traffic scenarios.
  • Effective joint optimization of V2V and V2N resources is achievable with advanced RL techniques.