A wavelength routing algorithm for optical satellite network based on deep reinforcement learning

被引：0
|
作者：
Li X. [1 ]
Li Y. [1 ]
Zhao S. [1 ]
机构：
[1] Information and Navigation College, Air Force Engineering University, Xi'an
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 01期
关键词：
deep reinforce learning; optical satellite network; service quality; wavelength routing;
D O I：
10.12305/j.issn.1001-506X.2023.01.31
中图分类号：
学科分类号：
摘要：
A method for optical satellite network wavelength routing which is based on deep reinforcement learning is proposed, aiming at the problems of slow route convergence and low wavelength utilization caused by the dynamic changes network topology, business diversification, and uneven load. Based on the software-defined (medium earth orbit/low earth orbit, MEO/LEO) two-layer satellite network architecture, the deep reinforcement learning algorithm is used to dynamically perceive the current network traffic load and link status, and a reward function based on delay, wavelength utilization and packet loss rate is constructed to make routing decisions. In order to solve the impact of a single-hop link on the entire optical path, a link bottleneck factor is introduced to search for the optimal path that meets the quality of service (QoS) constraints. The research results show that compared with the traditional satellite network distributed routing algorithm (SDRA) algorithm and the Q-routing algorithm, the proposed algorithm reduces the network delay and packet loss rate, improves the wavelength utilization, and also reduces the blocking rate of high-priority services. © 2023 Chinese Institute of Electronics. All rights reserved.
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页码：264 / 270
页数：6
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