频谱知识图谱：面向未来频谱管理的智能引擎

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

Abstract

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.

Key words: spectrum management, knowledge graph, cognitive graph, cognitive radio, recommendation for spectrum usage, search on spectrum management, question answering for spectrum management

CLC Number:

TN92

Jiachen SUN, Jinlong WANG, Guoru DING, Jin CHEN, Yuping GONG. Spectrum knowledge graph: an intelligent engine facing future spectrum management[J]. Journal on Communications, 2021, 42(5): 1-12.

Figures/Tables 8

References 43

[1]	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.
[2]	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.
[3]	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.
[4]	HAYKIN S . Cognitive radio:brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2005,23(2): 201-220.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	KEJRIWAL M . Domain-specific knowledge graph construction[M]. Cham: Springer International Publishing, 2019.
[13]	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.
[14]	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.
[15]	COLLINS A M , QUILLIAN M R . Retrieval time from semantic memory[J]. Journal of Verbal Learning and Verbal Behavior, 1969,8(2): 240-247.
[16]	BERNERS-LEE T . Semantic Web roadmap[R].(1998-10-14)[2020-08-19].
[17]	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.
[18]	SOWA J F . Building large knowledge-based systems:representation and inference in the Cyc project[J]. Artificial Intelligence, 1993,61(1): 95-104.
[19]	MILLER G A . WordNet:a lexical database for English[J]. Communications of the ACM, 1995,38(11): 39-41.
[20]	LIU H , SINGH P . ConceptNet—a practical commonsense reasoning tool-kit[J]. BT Technology Journal, 2004,22(4): 211-226.
[21]	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.
[22]	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.
[23]	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.
[24]	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.
[25]	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.
[26]	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.
[27]	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.
[28]	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.
[29]	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.
[30]	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.
[31]	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.
[32]	HUANG H , HECK L , JI H . Leveraging deep neural networks and knowledge graphs for entity disambiguation[J]. arXiv Preprint,arXiv:1504.07678, 2015.
[33]	FRANCIS-LANDAU M , DURRETT G , KLEIN D . Capturing semantic similarity for entity linking with convolutional neural networks[J]. arXiv Preprint,arXiv:1604.00734, 2016.
[34]	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.
[35]	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.
[36]	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.
[37]	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.
[38]	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.
[39]	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.
[40]	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.
[41]	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.
[42]	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.
[43]	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.

Metrics

Recommended 0

No Suggested Reading articles found!

概念	含义	与知识图谱的联系	与知识图谱的区别
专家系统^[14]	一个具有大量的专门知识与经验的程序系统，由知识库和推理机两部分组成	对领域知识进行知识表示并形成知识库/集合	专家系统的知识库依靠专家手动获取知识，知识图谱则支持自动化构建
语义网络	一个带标识的有向图。图中节点表示各种事物、概念、情况、状态等，节点与节点间连接线表示各种语义联系、动作^[15]	图结构化的知识表示方法	语义网络缺乏形式化的语法规范和形式化的语义标准，概念与实体之间没有明显的区分，节点与边难以进行更加丰富的定义
本体	定义了某一领域内的专业词汇以及它们之间的关系，是对概念化的精确描述	提供了一种人、机器等不同主体间交流的语义基础	本体侧重于描述概念类别和通用关系，较体系化；知识图谱包含更多具体实例，反映的是本体基础知识的具体表现结果
语义网	将Web中数据以RDF与互联网本体语言（OWL, ontology Web language）来表示，建立网络数据之间的语义关系，使处理数据的机器能够像人一样理解网络上的信息，从而提供更好的网络服务^[16]	常采用基础数据模型RDF	语义网的表示对象主要是万维网上的文档，如超文本标记语言（HTML, hypertext markup language）文档、可扩展标记语言（XML, extensive markup language）文档；知识图谱中实体的含义和类型更丰富

概念类别	实体及其属性
资源类	频段[频率范围、业务类型]
	信道[带宽、中心频率、占空比]
	通信设备[名称、工作频段、所处位置、发射功率、调制方式]
	电台[工作方式]
	雷达设备[名称、工作频段、所处位置、发射功率]
设备类	雷达[信号形式、天线类型、扫描方式、角跟踪方式]
	导航设备[名称、工作频段、发射功率]
	卫星[轨道高度、是否同步、轨道偏心率，近地点幅角]
	电抗设备[名称、身份、工作频段、发射功率]
	干扰武器[搭载平台、干扰模式]
	频谱秩序管理
	黑广播检测
	窃听器检测
	频谱对抗管理
场景类	电磁欺骗
	电磁干扰
	频谱共享管理
	空地频谱共享
	通信雷达频谱共享
	频谱感知
技术类	频谱预测
	频谱决策

关系类型	头实体类型	尾实体类型
属于	—	—
通信	设备类	设备类
干扰	设备类	设备类
使用	设备类	资源类

数据集	RMSE/dB
数据集	知识1	知识1+知识2
数据集1	-3.944 4	-4.059 9
数据集2	0.460 4	0.447 9

Spectrum knowledge graph: an intelligent engine facing future spectrum management

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 43

Related Articles 15

Metrics

Recommended 0

[1]	Rongpeng LI, Bingyan WANG, Honggang ZHANG, Zhifeng ZHAO. Design of knowledge enhanced semantic communication receiver [J]. Journal on Communications, 2023, 44(6): 70-76.
[2]	Zexi XU, Lei ZHUANG, Kunli ZHANG, Mingyu GUI. Online placement algorithm of service function chain based on knowledge graph [J]. Journal on Communications, 2022, 43(8): 41-51.
[3]	Xiaoguang LI, Lei GONG, Xiaoli LI, Xin ZHANG, Ge YU. Learner preferences prediction with mixture embedding of knowledge and behavior graph [J]. Journal on Communications, 2021, 42(8): 130-138.
[4]	Xiaojuan ZHAO, Yan JIA, Aiping LI, Kai CHEN. Research on link prediction model based on hierarchical attention mechanism [J]. Journal on Communications, 2021, 42(3): 36-44.
[5]	Bin LYU, Yi CAO, Jian LI, Ting QI. Performance enhancement scheme for IRS-assisted cognitive backscatter communication network [J]. Journal on Communications, 2021, 42(12): 172-181.
[6]	Yin LU,Jirong CHEN,Haowei BIAN,Hongbo ZHU. Novel opportunistic cooperative multicast scheme for NOMA [J]. Journal on Communications, 2020, 41(11): 141-150.
[7]	Zhen YANG,Mengyao ZHU,Youhong FENG. Secrecy performance analysis on cooperative CR-NOMA network [J]. Journal on Communications, 2020, 41(10): 139-147.
[8]	Yongjun XU,Yang YANG,Qilie LIU,Qianbin CHEN,Jinzhao LIN. Robust power and subcarrier allocation algorithm for cognitive network based on interference efficiency maximization [J]. Journal on Communications, 2020, 41(1): 84-93.
[9]	Xiaoqing DONG,Lianglun CHENG,Gengzhong ZHENG,Tao WANG. Spectrum resource allocation method of maximizing transmission rate in cognitive heterogeneous wireless networks [J]. Journal on Communications, 2019, 40(9): 124-135.
[10]	Min JIA,Xiaoye JING,Xiaofeng LIU,Feng LIU,Qing GUO,Xuemai GU. Spectrum allocation method for cognitive satellite network based on service priorities [J]. Journal on Communications, 2019, 40(4): 140-148.
[11]	Jisheng XU,Fanzi ZENG,Kang LI,Yongfeng LI. Performance analysis of two-way relay cooperation underlay cognitive radio networks based on WIPT [J]. Journal on Communications, 2019, 40(2): 129-136.
[12]	Yu LIU,Fanggang WANG,Jingwen ZHANG,Bo AI,Zhangdui ZHONG. EM-based blind LDPC identification in multipath channels [J]. Journal on Communications, 2018, 39(9): 43-48.
[13]	Yan LONG,Xiaoqian ZHANG,Xuming FANG,Rong HE. Resource allocation in cognitive radio network with energy harvesting [J]. Journal on Communications, 2018, 39(9): 67-75.
[14]	Benshun YI,Weiqing YAO. Degree distribution optimization for LT codes and its application in link maintenance of cognitive radio [J]. Journal on Communications, 2018, 39(4): 76-83.
[15]	Xianzhong XIE,Ying LUO,Ke YAN,Jiujiu CHEN. Recent advances and future challenges of four key resources cooperation in cognitive radio network [J]. Journal on Communications, 2018, 39(2): 149-163.