Journal on Communications ›› 2022, Vol. 43 ›› Issue (10): 133-145.doi: 10.11959/j.issn.1000-436x.2022197
• Papers • Previous Articles Next Articles
Xin SU1, Leilei MENG1, Yiqing ZHOU2,3, Wu CELIMUGE4
Revised:
2022-09-10
Online:
2022-10-25
Published:
2022-10-01
Supported by:
CLC Number:
Xin SU, Leilei MENG, Yiqing ZHOU, Wu CELIMUGE. Maritime mobile edge computing offloading method based on deep reinforcement learning[J]. Journal on Communications, 2022, 43(10): 133-145.
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文献 | 应用场景 | 系统模型 | 卸载算法 | 是否基于人工智能 | 是否考虑异构性 | 节点是否分层 | 实现效率 | 适用规模 | 是否存在维数灾难 |
文献[ | 蜂窝网 | 单服务器 | Stackelberg博弈 | 否 | 否 | 否 | 低 | 小 | 是 |
文献[ | 蜂窝网 | 多服务器 | 逐次逼近 | 否 | 是 | 否 | 低 | 小 | 是 |
文献[ | 蜂窝网 | 单服务器 | 启发式 | 否 | 否 | 否 | 低 | 小 | 是 |
文献[ | 蜂窝网 | 单服务器 | 动态规划 | 否 | 否 | 否 | 较低 | 小 | 是 |
文献[ | 蜂窝网 | 多服务器 | 迭代算法 | 否 | 是 | 否 | 较低 | 小 | 是 |
文献[ | 车联网 | 多服务器 | 预测卸载 | 否 | 是 | 否 | 低 | 小 | 是 |
文献[ | 车联网 | 多服务器 | 启发式 | 否 | 是 | 否 | 较低 | 小 | 是 |
文献[ | 车联网 | 多服务器 | 移动感知 | 否 | 是 | 否 | 低 | 小 | 是 |
文献[ | 车联网 | 多服务器 | Lyapunov优化 | 否 | 是 | 否 | 较低 | 小 | 是 |
文献[ | 车联网 | 多服务器 | 启发式 | 否 | 否 | 否 | 较低 | 小 | 是 |
文献[ | 车联网 | 单服务器 | Q-Learning | 是 | 否 | 否 | 较高 | 小 | 是 |
文献[ | 蜂窝网 | 多服务器 | Q-Learning | 是 | 否 | 否 | 较高 | 小 | 是 |
文献[ | 蜂窝网 | 多服务器 | Q-Learning,SDN | 是 | 是 | 否 | 较高 | 小 | 是 |
文献[ | 混合MEC网络 | 多服务器 | DNN | 是 | 是 | 否 | 较高 | 大 | 否 |
文献[ | 车联网 | 多服务器 | DRL | 是 | 是 | 否 | 高 | 大 | 否 |
文献[ | 蜂窝网 | 多服务器 | DRL | 是 | 是 | 否 | 高 | 大 | 否 |
文献[ | 海洋网络 | 单服务器 | 启发式 | 否 | 否 | 否 | 低 | 小 | 是 |
文献[ | 海洋网络 | 多服务器 | 粒子群优化、遗传算法 | 否 | 是 | 否 | 低 | 小 | 是 |
文献[ | 海洋网络 | 单服务器 | 人工鱼群算法 | 否 | 是 | 否 | 低 | 小 | 是 |
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