基于知识图谱的6G网络场景认知研究

赵茁乔; 承楠; 陈劼; 陈芳炯; 李长乐

doi:10.11959/j.issn.2096-6652.202339

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基于知识图谱的6G网络场景认知研究

学术论文 | 更新时间：2024-04-07

- 基于知识图谱的6G网络场景认知研究
- Research on 6G network scenario cognition based on knowledge graph
- 智能科学与技术学报 2023年5卷第4期页码：494-504
- 作者机构：
  
  1.西安电子科技大学通信工程学院，陕西西安 710071
  2.电子科技大学通信抗干扰技术国家级重点实验室，四川成都 610054
  3.华南理工大学电子与信息学院，广东广州 510640
- 作者简介：
  
  [ "赵茁乔（1998- ），男，西安电子科技大学通信工程学院硕士生，主要研究方向为知识图谱在通信领域的应用。" ]
  [ "承楠（1987- ），男，博士，西安电子科技大学通信工程学院教授、博士生导师。主要研究方向为智能网联汽车与先进交通系统、无人驾驶、空天地一体化网络技术，人工智能与大数据在网络中的应用，5G、B5G、6G先进网络技术研究与探索。" ]
  [ "陈劼（1973- ），女，博士，电子科技大学通信抗干扰技术国家级重点实验室副研究员，主要研究方向为认知无线电关键技术与应用及宽带多媒体集群。" ]
  [ "陈芳炯（1975- ），男，博士，华南理工大学电子与信息学院教授、博士生导师。主要研究方向为无线通信及组网技术，覆盖陆地及水下网络，具体包括信道估计与均衡、新型调制技术、声电协同组网等。" ]
  [ "李长乐（1976- ），男，博士，西安电子科技大学通信工程学院教授、副院长、博士生导师。主要研究方向为网联网控无人驾驶、智能网联汽车超视距感知、交通大数据分析及应用、大规模网络技术、高动态网络技术等。" ]
- 基金信息：
  
  国家重点研发计划(2020YFB1807700)
- DOI：10.11959/j.issn.2096-6652.202339
  中图分类号： TP391.1
- 收稿：2023-02-23，
  
  修回：2023-05-02，
  
  纸质出版：2023-12-15
- 稿件说明：
移动端阅览
赵茁乔, 承楠, 陈劼, 等. 基于知识图谱的6G网络场景认知研究[J]. 智能科学与技术学报, 2023,5(4):494-504.

ZHAO Zhuoqiao, CHENG Nan, CHEN Jie, et al. Research on 6G network scenario cognition based on knowledge graph[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 494-504.
赵茁乔, 承楠, 陈劼, 等. 基于知识图谱的6G网络场景认知研究[J]. 智能科学与技术学报, 2023,5(4):494-504. DOI： 10.11959/j.issn.2096-6652.202339.

ZHAO Zhuoqiao, CHENG Nan, CHEN Jie, et al. Research on 6G network scenario cognition based on knowledge graph[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 494-504. DOI： 10.11959/j.issn.2096-6652.202339.

摘要

6G网络覆盖空天地海全域，对于多样化、个性化的场景，6G网络需要提供定制化的服务，即按需服务。为实现全域全场景的按需服务，对场景特性进行精准、实时、智能的认知是一个重要前提。如何使网络自主智能地认知不同的场景及服务并将认知结果转换成场景特定的网络关键性能指标，并进一步对网络资源进行高效调度是亟待解决的关键问题。为此，将知识图谱应用于网络场景的认知识别，形成对6G网络场景的规范化描述，并构建了基于6G场景本体的知识图谱。同时，提出一种基于知识图谱嵌入的场景认知推理方法，实现了对图谱节点和关系的嵌入学习，并对场景特征节点进行了预测推理，取得了较高的准确度。该方法有助于实现6G全场景网络中场景感知、认知、按需服务的服务全生命周期自主管控，对于提高下一代网络的自主性和智能性有重要的指导意义。

Abstract

The 6G network covers the entire space

air

ground

and sea. For diversified and personalized scenarios

the 6G network needs to provide customized services

that is

on-demand services. In order to realize on-demand services in all domains and scenarios

accurate

real-time

and intelligent cognition of the characteristics of the scenarios is an important prerequisite. How to enable the network to autonomously and intelligently recognize different scenarios and services

convert them into scenario-specific network key performance indicator (KPI)

and further efficiently schedule network resources is a key problem that urgently needs to be solved. This paper applies the knowledge graph to the cognitive recognition of network scenarios

forms a standardized description of 6G network scenarios

and builds a knowledge graph based on the 6G scenario ontology. At the same time

a scene cognition reasoning method based on knowledge graph embedding is proposed

which realizes the embedding learning of graph nodes and relationships and reasons about scene feature nodes

achieving high accuracy. The method proposed in this paper helps to realize the autonomous control of the service life cycle of scene awareness

cognition

and on-demand services in the 6G full-scenario network

and has important innovation and guiding significance for improving the autonomy and intelligence of the next-generation network.

关键词

Keywords

references

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