面向6G的跨域知识驱动网络元智能算法框架

林佳琦; 钱琪杰; 钟旭东; 冯涛; 高先明; 葛嘉鑫; 彭木根; 任保全

doi:10.11959/j.issn.2096-3750.2026.00503

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面向6G的跨域知识驱动网络元智能算法框架

理论与技术 | 更新时间：2026-04-16

- 面向6G的跨域知识驱动网络元智能算法框架
- Cross-domain knowledge-driven meta-intelligent network algorithm framework for 6G
- 物联网学报 2026年10卷第1期页码：81-98
- 作者机构：
  
  1.北京邮电大学网络与交换技术全国重点实验室，北京 100876
  2.军事科学院系统工程研究院，北京 100141
  3.南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
  
  [ "林佳琦（1999‒ ），男，北京邮电大学网络与交换技术全国重点实验室博士生，军事科学院系统工程研究院联合培养，主要研究方向为智能网络、知识驱动网络。" ]
  [ "钱琪杰（1997‒ ），男，南京邮电大学通信与信息工程学院博士生，主要研究方向为人工智能、物联网、未来网络。" ]
  [ "钟旭东（1991‒ ），男，博士，军事科学院系统工程研究院副研究员，主要研究方向为智能信息网络技术、卫星通信、无线资源管理与优化。" ]
  [ "冯涛（1979‒ ），男，博士，军事科学院系统工程研究院正高级工程师、硕士生导师，主要研究方向为网络人工智能、软件定义网络、网络管理。" ]
  [ "高先明（1988‒ ），男，博士，军事科学院系统工程研究院高级工程师，主要研究方向为智能网络、韧性网络。" ]
  [ "葛嘉鑫（1990‒），女，军事科学院系统工程研究院工程师，主要研究方向为信号处理、人工智能。" ]
  [ "彭木根（1978‒ ），男，博士，北京邮电大学网络与交换技术全国重点实验室教授、博士生导师，主要研究方向为6G、空间信息通信、通感算一体化、通算融合无线电接入网络。" ]
  [ "任保全（1974‒ ），男，博士，军事科学院系统工程研究院研究员、博士生导师，主要研究方向为物联网、无线通信、移动通信网络技术。" ]
- 基金信息：
  
  国防重点实验室基金重点资助项目(6142006240401)
- DOI：10.11959/j.issn.2096-3750.2026.00503
  中图分类号： TN915.5
- 收稿：2025-05-14，
  
  修回：2025-07-02，
  
  录用：2025-07-18，
  
  纸质出版：2026-03-30
- 稿件说明：
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林佳琦,钱琪杰,钟旭东等.面向6G的跨域知识驱动网络元智能算法框架[J].物联网学报,2026,10(01):81-98.

Lin Jiaqi,Qian Qijie,Zhong Xudong,et al.Cross-domain knowledge-driven meta-intelligent network algorithm framework for 6G[J].Chinese Journal on Internet of Things,2026,10(01):81-98.
林佳琦,钱琪杰,钟旭东等.面向6G的跨域知识驱动网络元智能算法框架[J].物联网学报,2026,10(01):81-98. DOI： 10.11959/j.issn.2096-3750.2026.00503.

Lin Jiaqi,Qian Qijie,Zhong Xudong,et al.Cross-domain knowledge-driven meta-intelligent network algorithm framework for 6G[J].Chinese Journal on Internet of Things,2026,10(01):81-98. DOI： 10.11959/j.issn.2096-3750.2026.00503.

摘要

为应对现有自动化运维模型在6G多场景、实时化智能管理中的能力瓶颈，提出了一种跨域知识驱动的网络元智能算法框架。现有方案多依赖静态规则或单域优化，难以适配6G网络在复杂环境感知、动态策略迁移与多目标调度方面的综合需求。该框架将网络状态建模为环境域、网络域与用户行为域3类知识源，基于轻量化模型与图神经网络实现高层意图解析与跨域知识的在线融合，并通过知识蒸馏机制动态地更新全局知识库。在此基础上构建多层网络元智能体，形成“感知→推理→知识生成→决策下发→验证优化→记忆检索”的闭环控制流程，辅以自监督、强化与元学习，实现策略的快速迁移与持续演进。围绕低空交通管控场景，设计了3类典型任务：跨域组网、意图引导的智能体管理与蜂群路径规划。实验结果表明，所提方法在吞吐量、故障恢复时间、流量预测精度、决策时延、执行成功率、资源公平度、路径效率与任务成功率等关键指标上均取得了显著的提升。

Abstract

A cross-domain knowledge-driven meta-intelligent network algorithm framework was proposed in this paper to address the limitations of existing automated operation and maintenance models in supporting multi-scenario and real-time intelligent management in 6G networks. Traditional approaches often rely on static rules or single-domain optimization

which are insufficient for 6G demands such as heterogeneous perception

dynamic policy adaptation

and multi-objective scheduling. The framework modeled network states across environmental

network

and user behavior domains

leveraging lightweight models and graph neural networks for high-level intent parsing and online knowledge fusion. A global knowledge base was dynamically updated via knowledge distillation. Multi-layer meta-intelligent agents formed a closed-loop control process of perception

reasoning

knowledge generation

decision issuance

validation

and memory retrieval. Self-supervised learning

reinforcement learning

and meta-learning techniques were integrated to support rapid policy adaptation and continual optimization. Centered on a low-altitude traffic control scenario

the framework was evaluated through three tasks: knowledge-driven networking

intent-guided agent management

and swarm path planning. Experimental results show that the proposed method consistently outperforms baseline approaches in throughput

failure recovery time

traffic prediction accuracy

decision latency

execution success rate

resource fairness

path efficiency

and task success rate.

关键词

Keywords

references

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