Recursive deep reinforcement learning-based collaborative caching relay algorithm in mobile vehicular edge network
Considering scenarios without road side unit coverage, a recursive deep reinforcement learning-based collaborative caching relay algorithm was proposed to construct a caching system by leveraging the cooperation among vehicles. Recognizing the dynamic nature of caching decisions, the problem was mod...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2024-11-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024195/ |
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| Summary: | Considering scenarios without road side unit coverage, a recursive deep reinforcement learning-based collaborative caching relay algorithm was proposed to construct a caching system by leveraging the cooperation among vehicles. Recognizing the dynamic nature of caching decisions, the problem was modeled as a partially observable Markov decision process. Vehicle trajectories were predicted using graph neural network, and the connectivity stability between vehicles was measured to select those that could serve as caching nodes. In addition, long short-term memory network was integrated into the deep deterministic policy gradient algorithm to achieve the final caching decision. Simulation results demonstrate that the proposed algorithm outperforms traditional caching algorithms in terms of cache hit ratio and latency. |
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| ISSN: | 1000-436X |