首页|期刊导航|天地一体化信息网络|基于环境可靠性预测的低轨卫星网络弹性智能路由技术

基于环境可靠性预测的低轨卫星网络弹性智能路由技术OA

Resilient Intelligent Routing Technology for LEO Satellite Networks Based on Environmental Reliability Prediction

中文摘要英文摘要

低轨卫星网络在复杂空间环境中面临严峻的可靠性挑战,而传统路由协议多采用被动式响应机制,难以应对空间环境效应导致的链路不稳定风险.为此,提出一种基于环境可靠性预测的低轨卫星网络弹性智能路由方法.首先设计融合长期预测性辐射风险、瞬态空间天气事件与实时链路通信质量的空间环境可靠性指数,并给出端到端路由问题模型;其次构建基于消息传递机制的星间邻域状态信息聚合模型,提出环境状态预测下基于多智能体协同的分布式路由决策算法,实现低轨卫星网络路由决策从"被动响应"向"主动规避"的转变.仿真结果显示,相较于基准算法,该方法能主动规避高风险区域,有效提升动态空间环境下的低轨卫星网络端到端路由可靠性.

Low earth orbit satellite networks face severe reliability challenges in the complex space environment,where conventional routing protocols,with their reactive mechanisms,struggle to address the link instability risks induced by space environment effects.To this end,this paper proposes a resilient and intelligent routing method for low earth orbit satellite networks,driven by environmental re-liability prediction.The method begins by designing a space environment reliability index that integrates predictable radiation risks,space weather disturbances,and real-time link communication quality,and subsequently formulates the end-to-end routing problem.Next,a message-passing-based model is constructed to aggregate neighborhood state information among satellites.This enables a col-laborative multi-agent distributed routing algorithm,guided by environmental state predictions,to facilitate a paradigm shift in routing decisions from reactive response to proactive avoidance.Simulation results demonstrate that,compared to baseline algorithms,the pro-posed method can proactively avoid high-risk regions,significantly enhancing the end-to-end routing reliability of low earth orbit satel-lite networks in dynamic space environments.

陈啸;纪哲;吴胜;姬思敬;盛敏

北京邮电大学泛网无线通信教育部重点实验室,北京 100876北京邮电大学泛网无线通信教育部重点实验室,北京 100876北京邮电大学泛网无线通信教育部重点实验室,北京 100876西安电子科技大学空天地一体化综合业务网全国重点实验室,陕西 西安 710071西安电子科技大学空天地一体化综合业务网全国重点实验室,陕西 西安 710071

信息技术与安全科学

低轨卫星网络空间环境效应可靠性预测端到端路由多智能体强化学习

low earth orbit satellite networkspace environment effectreliability predictionend-to-end routingmulti-agent reinforce-ment learning

《天地一体化信息网络》 2026 (1)

38-48,11

国家自然科学基金(No.62495020,No.62495024,No.62201085) The National Natural Science Foundation of China(No.62495020,No.62495024,No.62201085)

10.11959/j.issn.2096-8930.2026005

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