现代信息科技2026，Vol.10Issue(11)：14-20,7.DOI:10.19850/j.cnki.2096-4706.2026.11.004
基于图神经网络与深度Q学习的低轨卫星路由算法
Low-Earth-Orbit Satellite Routing Algorithm Based on Graph Neural Networks and Deep Q-Learning
许向阳 1谷雨 1姜慧丽 1董俭奥1
作者信息
- 1. 河北科技大学 信息科学与工程学院,河北 石家庄 050018
- 折叠
摘要
Abstract
To address the challenges of rapid topology changes,unstable link conditions,and the difficulty of simultaneously guaranteeing multi-service Quality of Service(QoS)in low-Earth-orbit satellite networks,this paper studies an intelligent routing optimization method for multi-service scenarios.First,link state awareness is achieved through the HELLO message mechanism,to acquire information on link delay,bandwidth utilization,and congestion.Second,a Graph Neural Network(GNN)is employed to extract state representations of network topology and link features.On this basis,a Deep Q-Network(DQN)is introduced for routing decisions,and multi-objective QoS constraints are integrated into the reward function to achieve differentiated optimization for diverse service requirements.Simulation results demonstrate that the proposed method outperforms existing approaches in terms of average end-to-end delay,throughput,and packet loss rate,and can effectively enhance transmission efficiency and service quality of Low-Earth-Orbit satellite networks in dynamic environments.关键词
低轨卫星网络/多业务QoS/图神经网络/深度强化学习Key words
Low-Earth-Orbit satellite network/multi-service QoS/Graph Neural Networks/Deep Reinforcement Learning分类
信息技术与安全科学引用本文复制引用
许向阳,谷雨,姜慧丽,董俭奥..基于图神经网络与深度Q学习的低轨卫星路由算法[J].现代信息科技,2026,10(11):14-20,7.
