Deep Distributional Reinforcement Learning-Based Adaptive Routing With Guaranteed Delay Bounds
文献类型：期刊论文
| 作者 | Liu, Jianmin1; Li, Dan1,2; Xu, Yongjun3 |
| 刊名 | IEEE-ACM TRANSACTIONS ON NETWORKING
 |
| 出版日期 | 2024-07-15 |
| 页码 | 15 |
| 关键词 | Deep distributional reinforcement learning real-time QoS guarantees adaptive routing wireless multi-hop networks Deep distributional reinforcement learning real-time QoS guarantees adaptive routing wireless multi-hop networks |
| ISSN号 | 1063-6692 |
| DOI | 10.1109/TNET.2024.3425652 |
| 英文摘要 | Real-time applications that require timely data delivery over wireless multi-hop networks within specified deadlines are growing increasingly. Effective routing protocols that can guarantee real-time QoS are crucial, yet challenging, due to the unpredictable variations in end-to-end delay caused by unreliable wireless channels. In such conditions, the upper bound on the end-to-end delay, i.e., worst-case end-to-end delay, should be guaranteed within the deadline. However, existing routing protocols with guaranteed delay bounds cannot strictly guarantee real-time QoS because they assume that the worst-case end-to-end delay is known and ignore the impact of routing policies on the worst-case end-to-end delay determination. In this paper, we relax this assumption and propose DDRL-ARGB, an Adaptive Routing with Guaranteed delay Bounds using Deep Distributional Reinforcement Learning (DDRL). DDRL-ARGB adopts DDRL to jointly determine the worst-case end-to-end delay and learn routing policies. To accurately determine worst-case end-to-end delay, DDRL-ARGB employs a quantile regression deep Q-network to learn the end-to-end delay cumulative distribution. To guarantee real-time QoS, DDRL-ARGB optimizes routing decisions under the constraint of worst-case end-to-end delay within the deadline. To improve traffic congestion, DDRL-ARGB considers the network congestion status when making routing decisions. Extensive results show that DDRL-ARGB can accurately calculate worst-case end-to-end delay, and can strictly guarantee real-time QoS under a small tolerant violation probability against two state-of-the-art routing protocols. |
| 资助项目 | National Key Research and Development Program of China[2022YFB2901200] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001273051400001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39679]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Li, Dan |
| 作者单位 | 1.Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China 2.Zhongguancun Lab, Beijing 100095, Peoples R China 3.Inst Comp Technol, Chinese Acad Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jianmin,Li, Dan,Xu, Yongjun. Deep Distributional Reinforcement Learning-Based Adaptive Routing With Guaranteed Delay Bounds[J]. IEEE-ACM TRANSACTIONS ON NETWORKING,2024:15. |
| APA | Liu, Jianmin,Li, Dan,&Xu, Yongjun.(2024).Deep Distributional Reinforcement Learning-Based Adaptive Routing With Guaranteed Delay Bounds.IEEE-ACM TRANSACTIONS ON NETWORKING,15. |
| MLA | Liu, Jianmin,et al."Deep Distributional Reinforcement Learning-Based Adaptive Routing With Guaranteed Delay Bounds".IEEE-ACM TRANSACTIONS ON NETWORKING (2024):15. |
入库方式： OAI收割
来源：计算技术研究所
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