Caching Strategy Based on GAT-MADRL for Space-Air-Ground Integrated Vehicular Networks

ZHU Sifeng; SHI Kexuan; ZHAO Weifeng; ZHANG Zonghui; YU Youjian; ZHANG Qinghua; ZHU Hai

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Caching Strategy Based on GAT-MADRL for Space-Air-Ground Integrated Vehicular Networks

更新时间：2026-05-20

- Caching Strategy Based on GAT-MADRL for Space-Air-Ground Integrated Vehicular Networks
- Chinese Journal on Internet of Things (2026)
- 作者机构：
  
  1.天津城建大学计算机与信息工程学院，天津 300384
  2.天津城建大学图书馆，天津 300384
  3.河南工程学院计算机学院，郑州，河南 451191
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62172457);Postgraduate Innovation Project of Tianjin Chengjian University(2025YKC077;2025YKC079);Henan Provincial Science and Technology Development Program(252103810011;252103810206;261111211200)
- DOI：
  CLC： TP929.5
- Received：23 March 2026，
  
  Revised：2026-04-10，
  
  Accepted：18 May 2026，
- 稿件说明：
移动端阅览
ZHU Sifeng, SHI Kexuan, ZHAO Weifeng, et al. Caching Strategy Based on GAT-MADRL for Space-Air-Ground Integrated Vehicular Networks[J/OL]. Chinese Journal on Internet of Things, 2026.
DOI：

ZHU Sifeng, SHI Kexuan, ZHAO Weifeng, et al. Caching Strategy Based on GAT-MADRL for Space-Air-Ground Integrated Vehicular Networks[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

针对空天地融合车载网络中节点异构性强、拓扑动态变化频繁以及协同缓存困难等挑战，本文构建了面向空天地融合车载网络的有向异构图模型，设计了融合图注意力网络与多智能体深度强化学习的分布式缓存决策框架，提出了一种基于图注意力网络与多智能体深度强化学习的缓存策略优化方法。实验结果表明，与随机策略、GA策略、MADQN策略、MADDPG策略相比，所提的策略在任务平均时延方面分别降低了51.61%、38.12%、26.83%、22.12%，系统总能耗分别降低了71.68%、67.53%、43.23%、24.21%，缓存命中率分别提升了261.19%、49.11%、21.43%、6.21%。

Abstract

To address the challenges of strong node heterogeneity

frequent dynamic topology changes

and difficult cache coordination in Space-Air-Ground Integrated Vehicular Network (SAGVN)

this paper constructs a directed heterogeneous graph model for SAGVNs

designs a distributed caching decision framework that integrates Graph Attention Network (GAT) and Multi-Agent Deep Reinforcement Learning (MADRL)

and proposes a caching strategy optimization method based on GAT and MADRL. Experimental results show that

compared to the Random strategy

GA strategy

MADQN strategy

and MADDPG strategy

the proposed strategy reduces the average task delay by 51.61%

38.12%

26.83%

and 22.12%

lowers the total system energy consumption by 71.68%

67.53%

43.23%

and 24.21%

and improves the cache hit rate by 261.19%

49.11%

21.43%

and 6.21%

respectively.

关键词

Keywords

references

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