面向LEO Walker星座的AI预测驱动分层协同QoS保障机制

李毅; 乔艳珍; 周康燕; 刘立浩; 及志远; 赵鸿

doi:doi：10.11959/j.issn.1000-0801.2025041

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面向LEO Walker星座的AI预测驱动分层协同QoS保障机制

研究 | 更新时间：2026-01-09

- 面向LEO Walker星座的AI预测驱动分层协同QoS保障机制
- AI-Predicted Driven Hierarchically-Coordinated QoS Guarantee Mechanism for LEO Walker Constellation
- 天地一体化信息网络 2025年6卷第4期页码：84-95
- 作者机构：
  
  中国星网网络系统研究院有限公司，北京 100001
- 作者简介：
  
  [ "李毅（1984- ），男，硕士，中国星网网络系统究院有限公司高级工程师，主要研究方向为卫星通信、飞行器总体设计。" ]
  [ "乔艳珍（1983- ）女，硕士，现就职于中国星网网络系统研究院有限公司，主要研究方向为卫星互联网无线通信技术、空间网络组网设计。" ]
  [ "周康燕（1981- ），男，硕士，现就职于中国星网网络系统研究院，主要研究方向为卫星互联网总体设计和地面系统设计。" ]
  [ "刘立浩（1999- ），男，硕士，现就职于中国星网网络系统研究院有限公司，主要研究方向为卫星互联网系统设计、卫星通信核心算法设计。" ]
  [ "及志远（1998- ），男，硕士，现就职于中国星网网络系统研究院有限公司，主要研究方向为卫星互联网宽演体制、系统设计。" ]
  [ "赵鸿（1998- ），男，硕士，现就职于中国星网网络系统研究院有限公司，主要研究方向为卫星通信体制评估验证、卫星通信可维可测设计。" ]
- 基金信息：
- DOI：doi：10.11959/j.issn.1000-0801.2025041
  中图分类号： TP393
- 收稿：2025-06-07，
  
  修回：2025-07-22，
  
  纸质出版：2025-12-20
- 稿件说明：
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李毅,乔艳珍,周康燕等.面向LEO Walker星座的AI预测驱动分层协同QoS保障机制[J].天地一体化信息网络,2025,06(04):84-95.

LI Yi,QIAO Yanzhen,Zhou Kangyan,et al.AI-Predicted Driven Hierarchically-Coordinated QoS Guarantee Mechanism for LEO Walker Constellation[J].Space-Integrated-Ground Information Networks,2025,06(04):84-95.
李毅,乔艳珍,周康燕等.面向LEO Walker星座的AI预测驱动分层协同QoS保障机制[J].天地一体化信息网络,2025,06(04):84-95. DOI： doi：10.11959/j.issn.1000-0801.2025041.

LI Yi,QIAO Yanzhen,Zhou Kangyan,et al.AI-Predicted Driven Hierarchically-Coordinated QoS Guarantee Mechanism for LEO Walker Constellation[J].Space-Integrated-Ground Information Networks,2025,06(04):84-95. DOI： doi：10.11959/j.issn.1000-0801.2025041.

摘要

为提升LEO Walker星座的QoS保障能力，提出一种AI预测驱动分层协同QoS保障架构。该架构通过地面智能控制中心内置的融合AI引擎预测链路质量与业务需求，利用深度强化学习优化全局QoS与动态网络切片，并在卫星节点部署轻量级智能代理执行本地QoS保障。同时阐述系统架构、关键AI模型、深度强化学习优化、动态网络切片管理及分层协同控制。仿真结果表明该架构在时延、抖动、数据包传递率、资源利用率等性能上有显著提升。

Abstract

This paper proposes an AI-predicted driven hierarchically-coordinated QoS (AIP-HC-QoS) architecture for LEO Walker constellation. The architecture employs an AI engine in the ground intelligence and control center (GICC) for link quality and service demand prediction

while deep reinforcement learning optimizes global QoS and dynamic network slicing. Lightweight intelligent agents are deployed on satellite nodes for local QoS execution. This paper details the system architecture

key AI models

DRL optimization

dynamic network slicing management

and hierarchical coordination control. Simulations results indicate significant improvements in delay

jitter

PDR

resource utilization

etc.

关键词

Keywords

references

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星地异构网络联合QoS保障关键技术