Multi-service routing algorithm for satellite networks based on deep Q-network with long short-term memory

Wen Jinglong; Zhang Yi; Wei Debin; Pan Chengsheng

doi:10.11959/j.issn.1000-0801.DXKX250576

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Multi-service routing algorithm for satellite networks based on deep Q-network with long short-term memory

Research and Development | 更新时间：2026-06-17

- Multi-service routing algorithm for satellite networks based on deep Q-network with long short-term memory
- Telecommunications Science Vol. 42, Issue 5, Pages: 48-59(2026)
- 作者机构：
  
  1.大连大学信息工程学院，辽宁大连 116622
  2.南京邮电大学通信与网络技术国家工程研究中心，江苏南京 210023
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61931004)
- DOI：10.11959/j.issn.1000-0801.DXKX250576
  CLC： TP393;TN297
- Received：30 September 2025，
  
  Revised：2025-12-12，
  
  Accepted：25 December 2025，
  
  Published：20 May 2026
- 稿件说明：
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温京龙,张怡,魏德宾等.基于长短期记忆深度Q网络的卫星网络多业务路由算法[J].电信科学,2026,42(05):48-59.

Wen Jinglong,Zhang Yi,Wei Debin,et al.Multi-service routing algorithm for satellite networks based on deep Q-network with long short-term memory[J].Telecommunications Science,2026,42(05):48-59.
温京龙,张怡,魏德宾等.基于长短期记忆深度Q网络的卫星网络多业务路由算法[J].电信科学,2026,42(05):48-59. DOI： 10.11959/j.issn.1000-0801.DXKX250576.

Wen Jinglong,Zhang Yi,Wei Debin,et al.Multi-service routing algorithm for satellite networks based on deep Q-network with long short-term memory[J].Telecommunications Science,2026,42(05):48-59. DOI： 10.11959/j.issn.1000-0801.DXKX250576.

摘要

为了解决卫星网络中多业务服务质量保障与负载均衡的协同优化难题，提出了一种融合长短期记忆网络和深度Q网络的智能多业务路由算法。该算法把长短期记忆深度Q网络作为智能体部署在当前卫星节点，将当前节点与周围节点的链路时延、带宽、丢包率、流量、网络拓扑、业务类型作为网络状态输入智能体进行训练，输出动作是下一跳节点，使用带宽、时延、丢包率的加权和与最大链路带宽利用率的加权和作为奖励函数调整动作。智能体训练收敛后，卫星进行多业务传输。通过仿真实验和性能评估，研究结果表明所提出的算法在不同性能指标上均取得了显著成效，并且在平衡网络负载方面表现优异。

Abstract

To address the challenge of collaborative optimization between multi-service quality of service (QoS) guarantee and load balancing in satellite networks

an intelligent multi-service routing algorithm integrating long short-term memory with deep Q-Network was proposed. The algorithm deployed LSTM-DQN (LDQN) as an agent at the current satellite node

where the network state input to the agent includes link latency

bandwidth

packet loss rate

traffic

network topology and service type between the current node and its neighboring nodes. The output action was the next-hop node selection for the current node. The reward function

designed to guide action adjustment

combined the weighted sum of bandwidth

latency

and packet loss rate with the weighted sum of maximum link bandwidth utilization. After the agent’s training convergence

multi-service transmission was performed in the satellite network. Simulation experiments and performance evaluations demonstrate that the proposed algorithm achieves significant improvements across various performance metrics while exhibiting outstanding load balancing capabilities.

关键词

Keywords

references

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