Spectrum knowledge graph: an intelligent engine facing future spectrum management

Jiachen SUN; Jinlong WANG; Guoru DING; Jin CHEN; Yuping GONG

doi:10.11959/j.issn.1000-436x.2021084

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Spectrum knowledge graph: an intelligent engine facing future spectrum management

Papers | 更新时间：2024-06-06

- Spectrum knowledge graph: an intelligent engine facing future spectrum management
- Journal on Communications Vol. 42, Issue 5, Pages: 1-12(2021)
- 作者机构：
  
  陆军工程大学通信工程学院，江苏南京 210007
- 作者简介：
- 基金信息：
  
  The Science and Technology Innovation 2030-Key Project of “New Generation Artificial Intelligence” of China(2018AAA0102303);The National Natural Science Foundation of China(U20B2038);The National Natural Science Foundation of China(61871398);The National Natural Science Foundation of China(61931011);The Natural Science Founda-tion for Distinguished Young Scholars of Jiangsu Province(BK20190030)
- DOI：10.11959/j.issn.1000-436x.2021084
  CLC： TN92
- Online First：2021-05，
  
  Published：25 May 2021
- 稿件说明：
移动端阅览
Jiachen SUN, Jinlong WANG, Guoru DING, et al. Spectrum knowledge graph: an intelligent engine facing future spectrum management[J]. Journal on Communications, 2021, 42(5): 1-12.
DOI：

Jiachen SUN, Jinlong WANG, Guoru DING, et al. Spectrum knowledge graph: an intelligent engine facing future spectrum management[J]. Journal on Communications, 2021, 42(5): 1-12. DOI： 10.11959/j.issn.1000-436x.2021084.

摘要

针对当前频谱管理中表征方式较单一、管理方式对人的经验依赖性较强、管理效率和精准度较低等问题，面向未来频谱管理的自动化、智能化、精准化需求，将知识图谱理论与技术引入频谱管理中，给出了频谱知识图谱的概念和其依赖的频谱知识体系，以及三元组形式的表示方法，构建了由图谱层、设备层和场景层构成的基于频谱知识图谱的智能频谱管理框架，探讨了基于频谱知识图谱的用频推荐、频谱搜索、频谱问答等典型应用。仿真实验表明，频谱知识图谱能在用频推荐中发挥知识引导的作用。

Abstract

To solve the issues of simple representations on spectrum situation

much dependence on artificial experience in manual management and low efficiency and accuracy in the current spectrum management

meeting the requirements of automation

precision and real time for future spectrum management

the theory and technology of knowledge graph were introduced into spectrum management.The definition of spectrum knowledge graph

the knowledge schema it depends on and its representation in the form of triples were proposed.The intelligent spectrum management framework based on spectrum knowledge graph

consisting of the graph layer

the equipment layer and the scenario layer

was constructed.Typical applications based on spectrum knowledge graph were discussed

including the recommendation system for spectrum usage

the search engine on spectrum management

and question answering for spectrum management.Experiments demonstrate the spectrum knowledge graph can play a role of guidance by spectrum knowledge in spectrum usage recommendation.

关键词

Keywords

references

王先义 , 陈丹俊 , 刘斌 , 等 . 复杂电磁环境战场频谱管理 [J ] . 中国电子科学研究院学报 , 2008 , 3 ( 4 ): 338 - 344 .

WANG X Y , CHEN D J , LIU B , et al . Battlefield spectrum management in complex electromagnetic environment [J ] . Journal of China Academy of Electronics and Information Technology , 2008 , 3 ( 4 ): 338 - 344 .

王金龙 , 吴启晖 , 龚玉萍 . 认知无线网络 [M ] . 北京 : 机械工业出版社 , 2010 .

WANG J L , WU Q H , GONG Y P . Cognitive wireless network [M ] . Beijing : China Machine Press , 2010 .

SONG M , XIN C S , ZHAO Y X , et al . Dynamic spectrum access:from cognitive radio to network radio [J ] . IEEE Wireless Communications , 2012 , 19 ( 1 ): 23 - 29 .

HAYKIN S . Cognitive radio:brain-empowered wireless communications [J ] . IEEE Journal on Selected Areas in Communications , 2005 , 23 ( 2 ): 201 - 220 .

AKYILDIZ I F , LEE W Y , VURAN M C , et al . A survey on spectrum management in cognitive radio networks [J ] . IEEE Communications Magazine , 2008 , 46 ( 4 ): 40 - 48 .

梁应敞 , 谭俊杰 , Dusit Niyato . 智能无线通信技术研究概况 [J ] . 通信学报 , 2020 , 41 ( 7 ): 1 - 17 .

LIANG Y C , TAN J J , NIYATO D . Overview on intelligent wireless communication technology [J ] . Journal on Communications , 2020 , 41 ( 7 ): 1 - 17 .

YU L , CHEN J , DING G R , et al . Spectrum prediction based on Taguchi method in deep learning with long short-term memory [J ] . IEEE Access , 2018 , 6 : 45923 - 45933 .

孙佳琛 , 王金龙 , 陈瑾 , 等 . 群体智能协同通信:愿景、模型和关键技术 [J ] . 中国科学:信息科学 , 2020 , 50 ( 3 ): 307 - 317 .

SUN J C , WANG J L , CHEN J , et al . Cooperative communication based on swarm intelligence:vision,model,and key technology [J ] . Scientia Sinica (Informationis) , 2020 , 50 ( 3 ): 307 - 317 .

CHEN T , ZHANG H G , KATZ M D , et al . Swarm intelligence based dynamic control channel assignment in CogMesh [C ] // ICC Workshops- 2008 IEEE International Conference on Communications Workshops . Piscataway:IEEE Press , 2008 : 123 - 128 .

杨健 , 陈曦 , 丁国如 , 等 . 基于区块链的频谱设备网络中防御拜占庭攻击的分布式共识机制 [J ] . 通信学报 , 2020 , 41 ( 3 ): 1 - 16 .

YANG J , CHEN X , DING G R , et al . Blockchain-driven distributed consensus mechanism in defensing Byzantine attack for the Internet of spectrum device [J ] . Journal on Communications , 2020 , 41 ( 3 ): 1 - 16 .

王昊奋 , 漆桂林 , 陈华钧 . 知识图谱：方法、实践与应用 [M ] . 北京 : 电子工业出版社 , 2019 .

WANG H F , QI G L , CHEN H J . Knowledge graph:methods,practices and applications [M ] . Beijing : Publishing House of Electronics Industry , 2019 .

KEJRIWAL M . Domain-specific knowledge graph construction [M ] . Cham : Springer International Publishing , 2019 .

肖仰华 . 知识图谱：概念与技术 [M ] . 北京 : 电子工业出版社 , 2020 .

XIAO Y H . Knowledge graph:concept and technology [M ] . Beijing : Publishing House of Electronics Industry , 2020 .

张煜东 , 吴乐南 , 王水花 . 专家系统发展综述 [J ] . 计算机工程与应用 , 2010 , 46 ( 19 ): 43 - 47 .

ZHANG Y D , WU L N , WANG S H . Survey on development of expert system [J ] . Computer Engineering and Applications , 2010 , 46 ( 19 ): 43 - 47 .

COLLINS A M , QUILLIAN M R . Retrieval time from semantic memory [J ] . Journal of Verbal Learning and Verbal Behavior , 1969 , 8 ( 2 ): 240 - 247 .

BERNERS-LEE T . Semantic Web roadmap [R ] .(1998-10-14)[2020-08-19 ] .

WANG Q , MAO Z D , 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 .

SOWA J F . Building large knowledge-based systems:representation and inference in the Cyc project [J ] . Artificial Intelligence , 1993 , 61 ( 1 ): 95 - 104 .

MILLER G A . WordNet:a lexical database for English [J ] . Communications of the ACM , 1995 , 38 ( 11 ): 39 - 41 .

LIU H , SINGH P . ConceptNet—a practical commonsense reasoning tool-kit [J ] . BT Technology Journal , 2004 , 22 ( 4 ): 211 - 226 .

XU B , XU Y , LIANG J , et al . CN-DBpedia:a never-ending chinese knowledge extraction system [C ] // International Conference on Industrial,Engineering and Other Applications of Applied Intelligent Systems,Springer , 2017 : 428 - 438 .

侯梦薇 , 卫荣 , 陆亮 , 等 . 知识图谱研究综述及其在医疗领域的应用 [J ] . 计算机研究与发展 , 2018 , 55 ( 12 ): 2587 - 2599 .

HOU M W , WEI R , LU L , et al . Research review of knowledge graph and its application in medical domain [J ] . Journal of Computer Research and Development , 2018 , 55 ( 12 ): 2587 - 2599 .

MIAO R , ZHANG X , YAN H F , et al . A dynamic financial knowledge graph based on reinforcement learning and transfer learning [C ] // 2019 IEEE International Conference on Big Data . Piscataway:IEEE Press , 2019 : 5370 - 5378 .

丁铭 , 唐杰 . 从知识图谱到认知图谱:历史、发展与展望 [J ] . 中国计算机学会通讯 , 2020 , 16 ( 8 ): 11 - 18 .

DING M , TANG J . From knowledge graph to cognitive graph:history,development and vision [J ] . Communications of the CCF , 2020 , 16 ( 8 ): 11 - 18 .

DING M , ZHOU C , CHEN Q B , et al . Cognitive graph for multi-hop reading comprehension at scale [C ] // Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2019 : 2694 - 2703 .

张育瑜 , 赵磊 , 郭文彬 , 等 . 基于知识图谱的无线电监测及盲信号识别 [J ] . 无线电工程 , 2020 , 50 ( 3 ): 187 - 192 .

ZHANG Y Y , ZHAO L , GUO W B , et al . Radio monitoring and blind signal recognition based on knowledge graph [J ] . Radio Engineering , 2020 , 50 ( 3 ): 187 - 192 .

胡航宇 , 翟学萌 , 胡光岷 . 基于流知识图谱的通信网络流连接行为分析 [J ] . 计算机工程 , 2019 , 45 ( 11 ): 234 - 242 .

HU H Y , ZHAI X M , HU G M . Analysis of communication network flow connection behavior based on flow knowledge graph [J ] . Computer Engineering , 2019 , 45 ( 11 ): 234 - 242 .

AUMAYR E , WANG M X , BOSNEAG A M . Probabilistic knowledge-graph based workflow recommender for network management automation [C ] // 2019 IEEE 20th International Symposium on . Piscataway:IEEE Press , 2019 : 1 - 7 .

赵军 , 刘康 , 何世柱 . 知识图谱 [M ] . 北京 : 高等教育出版社 , 2018 .

ZHAO J , LIU K , HE S Z , et al . Knowledge graph [M ] . Beijing : Higher Education Press , 2018 .

LAMPLE G , BALLESTEROS M , SUBRAMANIAN S , et al . Neural architectures for named entity recognition [C ] // Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Stroudsburg:Association for Computational Linguistics,2016:arXiv Preprint,arXiv:1603 . 01360 , 2016 .

DERNONCOURT F , LEE J Y , SZOLOVITS P . NeuroNER:an easy-to-use program for named-entity recognition based on neural networks [C ] // Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing:System Demonstrations.Stroudsburg:Association for Computational Linguistics,2017:arXiv Preprint,arXiv:1705 . 05487 , 2017 .

HUANG H , HECK L , JI H . Leveraging deep neural networks and knowledge graphs for entity disambiguation [J ] . arXiv Preprint,arXiv:1504.07678 , 2015 .

FRANCIS-LANDAU M , DURRETT G , KLEIN D . Capturing semantic similarity for entity linking with convolutional neural networks [J ] . arXiv Preprint,arXiv:1604.00734 , 2016 .

ZENG D J , LIU K , CHEN Y B , et al . Distant supervision for relation extraction via piecewise convolutional neural networks [C ] // Proceedings of the Conference on Empirical Methods in Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2015 : 1753 - 1762 .

LIN Y K , SHEN S Q , LIU Z Y , et al . Neural relation extraction with selective attention over instances [C ] // Proceedings of the 54th Annual Meeting on Association for Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2016 : 2124 - 2133 .

CHEN Y B , XU L H , LIU K , et al . Event extraction via dynamic multi-pooling convolutional neural networks [C ] // Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2015 : 167 - 176 .

李涛 , 郭渊博 , 琚安康 . 融合对抗主动学习的网络安全知识三元组抽取 [J ] . 通信学报 , 2020 , 41 ( 10 ): 80 - 91 .

LI T , GUO Y B , JU A K . Knowledge triple extraction in cybersecurity with adversarial active learning [J ] . Journal on Communications , 2020 , 41 ( 10 ): 80 - 91 .

ZHANG J Z , CHEN Y , LIU Y X , et al . Spectrum knowledge and real-time observing enabled smart spectrum management [J ] . IEEE Access , 2020 , 8 : 44153 - 44162 .

赵泽亚 . 基于开放知识网络的关系推断技术研究 [D ] . 郑州:信息工程大学 , 2015 .

ZHAO Z Y . Research on relation inference in the open knowledge network [D ] . Zhengzhou:Information Engineering University , 2015 .

秦川 , 祝恒书 , 庄福振 , 等 . 基于知识图谱的推荐系统研究综述 [J ] . 中国科学:信息科学 , 2020 , 50 ( 7 ): 937 - 956 .

QIN C , ZHU H S , ZHUANG F Z , et al . A survey on knowledge graph-based recommender systems [J ] . Scientia Sinica (Informationis) , 2020 , 50 ( 7 ): 937 - 956 .

任国春 . 短波通信原理与技术 [M ] . 北京 : 机械工业出版社 , 2020 .

REN G C . Principle and technology of high frequency communication [M ] . Beijing : China Machine Press , 2020 .

LIN Y K , LIU Z Y , SUN M S , et al . Learning entity and relation embeddings for knowledge graph completion [C ] // Proceedings of AAAI . Palo Alto:AAAI Press , 2015 : 2181 - 2187 .

HAN X , CAO S L , LV X , et al . OpenKE:an open toolkit for knowledge embedding [C ] // Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing:System Demonstrations . Stroudsburg:Association for Computational Linguistics , 2018 : 1 - 6 .

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