Research on 6G network scenario cognition based on knowledge graph

ZHAO Zhuoqiao; CHENG Nan; CHEN Jie; CHEN Fangjiong; LI Changle

doi:10.11959/j.issn.2096-6652.202339

您当前的位置：

首页 >

文章列表页 >

Research on 6G network scenario cognition based on knowledge graph

Papers and Reports | 更新时间：2024-04-07

- Research on 6G network scenario cognition based on knowledge graph
- Chinese Journal of Intelligent Science and Technology Vol. 5, Issue 4, Pages: 494-504(2023)
- 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：
- 基金信息：
  
  National Key Research and Development Program of China(2020YFB1807700)
- DOI：10.11959/j.issn.2096-6652.202339
  CLC： TP391.1
- Received：23 February 2023，
  
  Revised：2023-05-02，
  
  Published：15 December 2023
- 稿件说明：
移动端阅览
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：

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

赵亚军 , 郁光辉 , 徐汉青 . 6G移动通信网络:愿景、挑战与关键技术 [J ] . 中国科学:信息科学 , 2019 , 49 ( 8 ): 963 - 987 .

ZHAO Y J , YU G H , XU H Q . 6G mobile communication networks: vision, challenges, and key technologies [J ] . Scientia Sinica(Informationis) , 2019 , 49 ( 8 ): 963 - 987 .

IMT-2030推进组 . IMT-2030(6G)推进组正式发布《6G总体愿景与潜在关键技术》白皮书 [J ] . 互联网天地 , 2021 ( 6 ): 8 - 9 .

IMT-2030 Promotion Group . IMT-2030 (6G) Promotion Group officially released the white paper, "6G Overall Vision and Potential Key Technologies "[J ] . China Internet , 2021 ( 6 ): 8 - 9 .

CHENG K X , ZHU L , YAO C H , et al . Squeeze-excitation based communication behavior recognition approach for spectrum sensing data [C ] // Proceedings of 2021 7th International Conference on Computer and Communications (ICCC) . Piscataway : IEEE Press , 2021 : 682 - 686 .

ZHOU T , WANG Y J , WANG C X , et al . Multi-feature fusion based recognition and relevance analysis of propagation scenes for high-speed railway channels [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 8 ): 8107 - 8118 .

WANG Y , WANG X , SHEN B , et al . Clustering optimization and HOG feature extraction based primary user activity scene recognition scheme [C ] // Proceedings of 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring) . Piscataway : IEEE Press , 2022 : 1 - 5 .

李建东 , 杨晶晶 , 黄铭 . 基于知识图谱的Wi-Fi室内定位技术研究 [J ] . 物联网学报 , 2022 , 6 ( 2 ): 19 - 25 .

LI J D , YANG J J , HUANG M . Research on Wi-Fi indoor positioning technology based on knowledge graph [J ] . Chinese Journal on Internet of Things , 2022 , 6 ( 2 ): 19 - 25 .

BAI Y L , WANG D , SONG B . A knowledge graph-based cooperative caching scheme in MEC-enabled heterogeneous networks [C ] // Proceedings of GLOBECOM 2022 - 2022 IEEE Global Communications Conference . Piscataway : IEEE Press , 2022 : 5959 - 5964 .

SONG G X , ZHENG M Z , XU H K , et al . Construction of 5G protocol knowledge graph based on multi-relation and sequential chain [C ] // Proceedings of 2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA) . Piscataway : IEEE Press , 2022 : 1374 - 1377 .

徐增林 , 盛泳潘 , 贺丽荣 , 等 . 知识图谱技术综述 [J ] . 电子科技大学学报 , 2016 , 45 ( 4 ): 589 - 606 .

XU Z L , SHENG Y P , HE L R , et al . Review on knowledge graph techniques [J ] . Journal of University of Electronic Science and Technology of China , 2016 , 45 ( 4 ): 589 - 606 .

STUDER R . Knowledge engineering: principles and methods [J ] . Data & Knowledge Engineering , 2008 , 25 ( 1-2 ) : 161 - 197 .

牟天昊 , 李少远 . 流程工业控制系统的知识图谱构建 [J ] . 智能科学与技术学报 , 2022 , 4 ( 1 ): 129 - 141 .

MOU T H , LI S Y . Knowledge graph construction for control systems in process industry [J ] . Chinese Journal of Intelligent Science and Technology , 2022 , 4 ( 1 ): 129 - 141 .

HUANG Z, XU W, YU K. Bidirectional LSTM-CRF models for sequence tagging[EB]. arXiv preprint, 2015, arXiv:1508.01991.

LIU X Y , TAN H B , CHEN Q H , et al . RAGAT: relation aware graph attention network for knowledge graph completion [J ] . IEEE Access , 2021 , 9 : 20840 - 20849 .

陈名杨 , 张文 , 陈湘楠 , 等 . 群体知识图谱:分布式知识迁移与联邦式图谱推理 [J ] . 智能科学与技术学报 , 2022 , 4 ( 1 ): 55 - 64 .

CHEN M Y , ZHANG W , CHEN X N , et al . Collective knowledge graph: meta knowledge transfer and federated graph reasoning [J ] . Chinese Journal of Intelligent Science and Technology , 2022 , 4 ( 1 ): 55 - 64 .

WANG Q , MAO Z , WANG B , et al . Knowledge graph embedding: a survey of approaches and applications [J ] . IEEE Transactions on Knowledge and Data Engineering , 2017 , 29 ( 12 ): 2724 - 2743 .

BORDES A , USUNIER N , GARCIA-DURÁN A , et al . Translating embeddings for modeling multi-relational data [C ] // Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2 . New York : ACM , 2013 : 2787 - 2795 .

WANG Z , ZHANG J W , FENG J L , et al . Knowledge graph embedding by translating on hyperplanes [C ] // Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence . New York : ACM , 2014 : 1112 - 1119 .

LIN Y K , LIU Z Y , SUN M S , et al . Learning entity and relation embeddings for knowledge graph completion [C ] // Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence . New York : ACM , 2015 : 2181 - 2187 .

DEVLIN J , CHANG M W , LEE K , et al . Bert: pre-training of deep bidirectional transformers for language understanding [C ] // Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . [ S.l. : s.n. ] , 2019 : 4171 - 4186 .

Views

508

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on well logging text generation method combining K-BERT and KG-BART

Design and implementation of an intelligent question answering system for Macrobrachium rosenbergii aquaculture based on knowledge graph and large language model

Parallel reasoning: a virtual-real interactive knowledge collaboration framework based on ACP approach

Robust non-negative supervised low-rank discriminant embedding algorithm

Intelligent innovative regulatory tools on financial technology:concept,platform framework,and prospects

Related Author

XIAO Yang

TIAN Mingjia

CAO Maojun

YANG Guowei

GUO Jianlin

LI Fei

ZHENG Hao

Jiachen HOU

Related Institution

School of Computer and Information Technology, Northeast Petroleum University

School of Communication Engineering, Hangzhou Dianzi University

Zhejiang Institute of Freshwater Fisheries

School of Artificial Intelligence, Anhui University

The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences

Postal code：100079
Tel：（010）53879206 Email：cjist@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：157206 Visits Today：32

⁰