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
相关论文
共 30 条
  • [11] GONG S L, SHEN H, ZHAO K L, Et al., Network availability maximization for free-space optical satellite communications, IEEE Wireless Communications Letters, 9, 3, pp. 411-415, (2020)
  • [12] LIU Q L, YAO J F, LIU Z G, Research on wavelength routing algorithm based on multi-service in satellite optical network, Journal of System Simulation, 29, 8, pp. 1780-1787, (2017)
  • [13] SUN X, CAO S Z, A routing and wavelength assignment algorithm based on two types of LEO constellations in optical satellite networks, Journal of Lightwave Technology, 38, 8, pp. 2106-2113, (2020)
  • [14] YANG Q L, TAN L Y, MA J, Analysis of crosstalk in optical satellite networks with wavelength division multiplexing architectures[J], Journal of Lightwave Technology, 28, 6, pp. 931-938, (2010)
  • [15] ZHOU R, HE L W, TANG C C, Et al., SDN path enhancement algorithm based on multi-objective constraint genetic algoithm[J], Computer Technology and Development, 7, pp. 17-22, (2019)
  • [16] GUO Q, GU R T, DONG T, Et al., SDN-based end-to-end fragment-aware routing for elastic data flows in LEO satellite-terrestrial network, IEEE Access, 7, pp. 396-410, (2019)
  • [17] WU Y., Research on intelligent SDN routing optimization technology based on traffic classification, (2019)
  • [18] SHI X D, LI Y J, ZHAO S H, Et al., Multi-QoS objective optimization routing algorithm of satellite network based on SDN, Systems Engineering and Electronics, 42, 6, pp. 1395-1401, (2020)
  • [19] CHENG Z J, ZHANG X N, LI Y C, Et al., Congestion-aware local reroute for fast failure recovery in software-defined networks, Journal of Optical Communications and Networking, 9, 11, pp. 934-944, (2017)
  • [20] PICCHI R, CHITI F, FANTACCI R, Et al., Towards quantum satellite internetworking: a software-defined networking perspective, IEEE Access, 8, pp. 210370-210381, (2020)
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