| 基于深度强化学习方法的无线多跳网络能量高效机会路由 |
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| 作者姓名： | 靳晓晗 岩延 张宝贤 |
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| 作者单位： | 中国科学院大学泛在与传感网研究中心, 北京 100049 |
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| 基金项目： | Supported by National Natural Science Foundation of China (61872331) and the Fundamental Research Funds for the Central Universities |
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| 摘 要： | 由于机会路由能够利用无线信道的广播特性和有损特性,因此一直是提高无线网络路由性能的一个很有效的途径。提出一种基于深度强化学习的无线多跳网络能量高效机会路由算法,该算法使得智能体能够通过训练学习最优的路由策略,以通过机会路由的方式减少传输时间,同时平衡能耗延长网络寿命。此外,本算法还可以极大地缓解冷启动问题并获得较好的初始性能。仿真结果表明,与现有算法相比,该算法具有更好的性能。
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| 关 键 词： | 深度强化学习 无线多跳网络 机会路由 |
| 收稿时间： | 2020-04-20 |
| 修稿时间： | 2020-05-12 |
Energy efficient opportunistic routing for wireless multihop networks: a deep reinforcement learning approach |
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| Authors: | JIN Xiaohan YAN Yan ZHANG Baoxian |
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| Institution: | Research Center of Ubiquitous Sensor Networks, University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | Opportunistic routing has been an efficient approach for improving the performance of wireless multihop networks due to its salient features to take advantage of the broadcast and lossy nature of wireless channels. In this paper, we propose a deep reinforcement learning based energy efficient opportunistic routing algorithm for wireless multihop networks, which enables a learning agent to train and learn optimized routing policy to reduce the transmission time while balancing the energy consumption to extend the life of the network in an opportunistic way. Furthermore, the proposed algorithm can significantly alleviate the cold start problem and achieve better initial performance. Simulation results demonstrate that the proposed algorithm yield better performance as compared with existing algorithms. |
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| Keywords: | deep reinforcement learning wireless multihop networks opportunistic routing |
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