Considering scenarios without road side unit coverage,a recursive deep reinforcement learning-based collab-orative caching relay algorithm was proposed to construct a caching system by leveraging the cooperation among ve-hicles.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 re-sults demonstrate that the proposed algorithm outperforms traditional caching algorithms in terms of cache hit ratio and latency.
关键词
车载边缘网络/协作缓存接力/递归深度强化学习/马尔可夫决策
Key words
vehicular edge network/collaborative caching relay/recursive deep reinforcement learning/Markov decision
维普
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