6G知识体系构建：面向全域全场景的学术知识挖掘及其按需应用

沙子凡; 承楠; 惠一龙; 岳文伟; 付宇钏; 孙瑞锦

doi:10.11959/j.issn.1000-436x.2023181

您当前的位置：

首页 >

文章列表页 >

6G知识体系构建：面向全域全场景的学术知识挖掘及其按需应用

综述 | 更新时间：2024-06-06

- 6G知识体系构建：面向全域全场景的学术知识挖掘及其按需应用
- 6G knowledge system construction: academic knowledge mining and on-demand application for full domains and omni scenarios
- 通信学报 2023年44卷第9期页码：173-187
- 作者机构：
  
  西安电子科技大学空天地一体化综合业务网全国重点实验室，陕西西安710071
- 作者简介：
  
  [ "沙子凡（1998- ），男，江苏扬州人，西安电子科技大学博士生，主要研究方向为6G通信网络、车联网、机器学习技术等" ]
  [ "承楠（1987- ），男，辽宁锦州人，博士，西安电子科技大学教授、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、6G先进网络技术" ]
  [ "惠一龙（1988- ），男，陕西西安人，博士，西安电子科技大学博士生导师，主要研究方向为车联网、无人驾驶及数字孪生系统" ]
  [ "岳文伟（1992- ），男，山西太原人，博士，西安电子科技大学讲师，主要研究方向为智能交通系统、无线传感器网络、大数据" ]
  [ "付宇钏（1992- ），女，陕西汉中人，博士，西安电子科技大学副教授，主要研究方向为车联网、智能驾驶等" ]
  [ "孙瑞锦（1992- ），女，山西运城人，博士，西安电子科技大学讲师，主要研究方向为知识驱动的无线网络多维资源按需调度、大规模高动态无人机集群自组织网络" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2020YFB1807700)
- DOI：10.11959/j.issn.1000-436x.2023181
  中图分类号： TN929.5
- 网络出版日期：2023-09，
  
  纸质出版日期：2023-09-25
- 稿件说明：
移动端阅览
沙子凡, 承楠, 惠一龙, 等. 6G知识体系构建：面向全域全场景的学术知识挖掘及其按需应用[J]. 通信学报, 2023,44(9):173-187.

Zifan SHA, Nan CHENG, Yilong HUI, et al. 6G knowledge system construction: academic knowledge mining and on-demand application for full domains and omni scenarios[J]. Journal on communications, 2023, 44(9): 173-187.
沙子凡, 承楠, 惠一龙, 等. 6G知识体系构建：面向全域全场景的学术知识挖掘及其按需应用[J]. 通信学报, 2023,44(9):173-187. DOI： 10.11959/j.issn.1000-436x.2023181.

Zifan SHA, Nan CHENG, Yilong HUI, et al. 6G knowledge system construction: academic knowledge mining and on-demand application for full domains and omni scenarios[J]. Journal on communications, 2023, 44(9): 173-187. DOI： 10.11959/j.issn.1000-436x.2023181.

摘要

当前 6G 相关概念并未统一，亟待一致性的认知和定义，学术和产业界对 6G 的发展全貌和相关领域研究进展缺少清晰认识。为此，构建了 6G 知识库及知识体系。首先，对现有 6G 学术文献进行自动化筛选和结构化存储；其次，在对文本数据进行标注和规范化的基础上构建了6G知识库；再次，利用6G知识库实现了对6G全领域的统计分析；利用自然语言处理、深度神经网络和潜在树模型等技术实现对6G知识的抽取和生成。最后，在大模型训练的基础上，面向多样化的服务需求实现按需的知识应用。

Abstract

At present

the concepts related to 6G have not been unified

and there is an urgent need for consistent cognition and definition.Academics and industries lack a clear understanding of the overall development of 6G and the research progress in related fields.Therefore

the 6G knowledge base and knowledge system was constructed.Firstly

the existing 6G academic documents were automatically screened and stored in a structured way.Secondly

a 6G knowledge base was constructed on the basis of labeling and standardizing text data.In addition

a comprehensive statistical analysis was conducted across all domains of 6G based on the knowledge base and the technologies such as natural language processing

deep neural network and latent tree model were used to realize the extraction and generation of 6G knowledge.Finally

on the basis of large-scale model training

the on-demand knowledge application was realized for diversified service requirements.

关键词

Keywords

references

3GPP . Release 17 description；summary of Rel-17 work items (TR 21.917) [R ] . 2021 .

LATVA-AHO M , LEPPÄNEN K . Key drivers and research challenges for 6G ubiquitous wireless intelligence [R ] . 2019 .

Samsung Research . 6G:the next hyper connected experience for all [R ] . 2020 .

YOU X H , WANG C X , HUANG J , et al . Towards 6G wireless communication networks:vision,enabling technologies,and new paradigm shifts [J ] . Science China Information Sciences , 2021 , 64 ( 1 ): 110301 .

GUO W S . Explainable artificial intelligence for 6G:improving trust between human and machine [J ] . IEEE Communications Magazine , 2020 , 58 ( 6 ): 39 - 45 .

ZHOU Y Q , TIAN L , LIU L , et al . Fog computing enabled future mobile communication networks:a convergence of communication and computing [J ] . IEEE Communications Magazine , 2019 , 57 ( 5 ): 20 - 27 .

ZHOU D , SHENG M , LI J D , et al . Aerospace integrated networks innovation for empowering 6G:a survey and future challenges [J ] . IEEE Communications Surveys ＆ Tutorials , 2023 , 25 ( 2 ): 975 - 1019 .

张彤 , 任奕璟 , 闫实 , 等 . 人工智能驱动的 6G 网络:智慧内生 [J ] . 电信科学 , 2020 , 36 ( 9 ): 14 - 22 .

ZHANG T , REN Y J , YAN S , et al . Artificial intelligence driven 6G networks:endogenous intelligence [J ] . Telecommunications Science , 2020 , 36 ( 9 ): 14 - 22 .

钱志鸿 , 肖琳 , 王雪 . 面向未来移动网络密集连接的关键技术综述 [J ] . 通信学报 , 2021 , 42 ( 4 ): 22 - 43 .

QIAN Z H , XIAO L , WANG X . Review on strategic technology of dense connection for the future mobile network [J ] . Journal on Communications , 2021 , 42 ( 4 ): 22 - 43 .

ZHOU Y , LIU L , WANG L , et al . Service-aware 6G:an intelligent and open network based on the convergence of communication,computing and caching [J ] . Digital Communications and Networks , 2020 , 6 ( 3 ): 253 - 260 .

DANG S P , AMIN O , SHIHADA B , et al . What should 6G be？ [J ] . Nature Electronics , 2020 , 3 ( 1 ): 20 - 29 .

SAAD W , BENNIS M , CHEN M Z . A vision of 6G wireless systems:applications,trends,technologies,and open research problems [J ] . IEEE Network , 2020 , 34 ( 3 ): 134 - 142 .

YANG P , XIAO Y , XIAO M , et al . 6G wireless communications:vision and potential techniques [J ] . IEEE Network , 2019 , 33 ( 4 ): 70 - 75 .

廖建新 , 付霄元 , 戚琦 , 等 . 6G-ADM:基于知识空间的6G网络管控体系 [J ] . 通信学报 , 2022 , 43 ( 6 ): 3 - 15 .

LIAO J X , FU X Y , QI Q , et al . 6G-ADM:knowledge based 6G network management and control architecture [J ] . Journal on Communications , 2022 , 43 ( 6 ): 3 - 15 .

LETAIEF K B , CHEN W , SHI Y M , et al . The roadmap to 6G:AI empowered wireless networks [J ] . IEEE Communications Magazine , 2019 , 57 ( 8 ): 84 - 90 .

CHOWDHURY M Z , SHAHJALAL M , AHMED S , et al . 6G wireless communication systems:applications,requirements,technologies,challenges,and research directions [J ] . IEEE Open Journal of the Communications Society , 2020 , 1 : 957 - 975 .

杜小勇 , 陈峻 , 陈跃国 . 大数据探索式搜索研究 [J ] . 通信学报 , 2015 , 36 ( 12 ): 77 - 88 .

DU X Y , CHEN J , CHEN Y G . Exploratory search on big data [J ] . Journal on Communications , 2015 , 36 ( 12 ): 77 - 88 .

TAN Z W , LIU C F , MAO Y N , et al . AceMap:a novel approach towards displaying relationship among academic literatures [C ] // Proceedings of the 25th International Conference Companion on World Wide Web . New York:ACM Press , 2016 : 437 - 442 .

KANG Y , CAI Z , TAN C W , et al . Natural language processing (NLP) in management research:a literature review [J ] . Journal of Management Analytics , 2020 , 7 ( 2 ): 139 - 172 .

QIU X P , SUN T X , XU Y G , et al . Pre-trained models for natural language processing:a survey [J ] . Science China Technological Sciences , 2020 , 63 ( 10 ): 1872 - 1897 .

LIU P F , YUAN W Z , FU J L , et al . Pre-train,prompt,and predict:asystematic survey of prompting methods in natural language processing [J ] . ACM Computing Surveys , 2023 , 55 ( 9 ): 1 - 35 .

CHEN X , JIA S , XIANG Y , et al . A review:knowledge reasoning over knowledge graph [J ] . Expert Systems With Applications , 2020 ,141:112948.

李青 , 钟将 , 李立力 , 等 . 基于预训练机制的自修正复杂语义分析方法 [J ] . 通信学报 , 2019 , 40 ( 12 ): 41 - 50 .

LI Q , ZHONG J , LI L L , et al . Self-correcting complex semantic analysis method based on pre-training mechanism [J ] . Journal on Communications , 2019 , 40 ( 12 ): 41 - 50 .

李晖 , 金家立 , 金纾羽 , 等 . 基于自动选择编码及动态选词策略的文本隐写方法 [J ] . 通信学报 , 2022 , 43 ( 9 ): 240 - 253 .

LI H , JIN J L , JIN S Y , et al . Text steganography method based on automatic selection coding and dynamic word selection strategy [J ] . Journal on Communications , 2022 , 43 ( 9 ): 240 - 253 .

冯海林 , 张潇 , 刘同存 . 融合评论文本特征和评分图卷积表示的推荐模型 [J ] . 通信学报 , 2022 , 43 ( 3 ): 164 - 171 .

FENG H L , ZHANG X , LIU T C . Recommendation model combining review’s feature and rating graph convolutional representation [J ] . Journal on Communications , 2022 , 43 ( 3 ): 164 - 171 .

DEVLIN J , CHANG M , LEE K , et al . BERT:pre-training of deep bidirectional transformers for language understanding [J ] . arXiv Preprint,arXiv:1810.04805 , 2018 .

SHE C Y , SUN C J , GU Z Y , et al . A tutorial on ultrareliable and low-latency communications in 6G:integrating domain knowledge into deep learning [J ] . Proceedings of the IEEE , 2021 , 109 ( 3 ): 204 - 246 .

GOU J P , YU B S , MAYBANK S J , et al . Knowledge distillation:a survey [J ] . International Journal of Computer Vision , 2021 , 129 ( 6 ): 1789 - 1819 .

MESTRES A , RODRIGUEZ-NATAL A , CARNER J , et al . Knowledge-defined networking [J ] . ACM SIGCOMM Computer Communication Review , 2017 , 47 ( 3 ): 2 - 10 .

XU M R , HOANG D T , KANG J W , et al . Secure and reliable transfer learning framework for 6G-enabled Internet of vehicles [J ] . IEEE Wireless Communications , 2022 , 29 ( 4 ): 132 - 139 .

SOUZA R , SANTOS M , BONFIM M , et al . KDN-based fault-tolerant scheduling for VNFs in data centers [J ] . IEEE Transactions on Network and Service Management , 2022 , 19 ( 4 ): 4905 - 4917 .

王敬宇 , 庄子睿 . 知识定义多模态网络按需服务体系研究 [J ] . 通信学报 , 2022 , 43 ( 4 ): 71 - 82 .

WANG J Y , ZHUANG Z R . Research on a knowledge-defined polymorphic network attainable service architecture [J ] . Journal on Communications , 2022 , 43 ( 4 ): 71 - 82 .

GHOSH S , DAGIUKLAS T , IQBAL M , et al . A cognitive routing framework for reliable communication in IoT for industry 5.0 [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 8 ): 5446 - 5457 .

CASTANEDA H L M , DUQUE T A , CAMPO M W Y . An approach based on knowledge-defined networking for identifying video streaming flows in 5G networks [J ] . IEEE Latin America Transactions , 2021 , 19 ( 10 ): 1737 - 1744 .

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 .

CABLEFREE . Beyond 5G:the roadmap to 6G and beyond [R ] . 2017 .

CHEN P X , ZHANG N L , LIU T F , et al . Latent tree models for hierarchical topic detection [J ] . Artificial Intelligence , 2017 , 250 : 105 - 124 .

COVER T M , THOMAS J A . Elements of information theory [M ] . 2nd Edition . San Francisco : John Wiley＆ Sons,Inc. , 2001 .

ROBERTSON S , ZARAGOZA H . The probabilistic relevance framework:BM25 and beyond [J ] . Foundations and Trends in Information Retrieval , 2009 , 3 ( 4 ): 333 - 389 .

YANG S , LIU Y , FENG D W , et al . Text generation from data with dynamic planning [J ] . IEEE/ACM Transactions on Audio,Speech,and Language Processing , 2022 , 30 : 26 - 34 .

YANG D C , YU J W , WANG H L , et al . Diffsound:discrete diffusion model for text-to-sound generation [J ] . IEEE/ACM Transactions on Audio,Speech,and Language Processing , 2023 , 31 : 1720 - 1733 .

TRISEDYA B D , QI J Z , WANG W , et al . GCP:graph encoder with content-planning for sentence generation from knowledge bases [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022 , 44 ( 11 ): 7521 - 7533 .

杨秀璋 , 彭国军 , 李子川 , 等 . 基于 Bert 和 BiLSTM-CRF 的 APT攻击实体识别及对齐研究 [J ] . 通信学报 , 2022 , 43 ( 6 ): 58 - 70 .

YANG X Z , PENG G J , LI Z C , et al . Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF [J ] . Journal on Communications , 2022 , 43 ( 6 ): 58 - 70 .

TANG M Y , GAO H C , ZHANG Y , et al . Research on deep learning techniques in breaking text-based captchas and designing image-based captcha [J ] . IEEE Transactions on Information Forensics and Security , 2018 , 13 ( 10 ): 2522 - 2537 .

STROBELT H , KINLEY J , KRUEGER R , et al . GenNI:human-AI collaboration for data-backed text generation [J ] . IEEE Transactions on Visualization and Computer Graphics , 2022 , 28 ( 1 ): 1106 - 1116 .

IQBAL T , QURESHI S . The survey:text generation models in deep learning [J ] . Journal of King Saud University-Computer and Information Sciences , 2022 , 34 ( 6 ): 2515 - 28 .

HOCHREITER S , SCHMIDHUBER J . Long short-term memory [J ] . Neural Computation , 1997 , 9 ( 8 ): 1735 - 1780 .

PAWADE D , SAKHAPARA A , JAIN M , et al . Story scrambler - automatic text generation using word level RNN-LSTM [J ] . International Journal of Information Technology and Computer Science , 2018 , 10 ( 6 ): 44 - 53 .

MEI J , WANG X B , ZHENG K . An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks [J ] . Intelligent and Converged Networks , 2020 , 1 ( 3 ): 281 - 294 .

CAO Y , LI S , LIU Y , et al . A comprehensive survey of AI-generated content (AIGC):a history of generative AI from GAN to ChatGPT [J ] . arXiv Preprint,arXiv:2303.04226 , 2023 .

GUNNING D , STEFIK M , CHOI J , et al . XAI-explainable artificial intelligence [J ] . Science Robotics , 2019 , 4 ( 37 ): eaay7120 .

浏览量

252

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向6G的深度图像语义通信模型

6G通信感知一体化网络的感知算法研究与优化

意图抽象与知识联合驱动的6G内生智能网络架构

面向零功耗物联网的反向散射通信综述

面向6G的跨模态信号重建技术