Artificial intelligence driven 6G networks:endogenous intelligence

Tong ZHANG; Yijing REN; Shi YAN; Mugen PENG

doi:10.11959/j.issn.1000-0801.2020266

您当前的位置：

首页 >

文章列表页 >

Artificial intelligence driven 6G networks:endogenous intelligence

Research and Development | 更新时间：2024-06-05

- Artificial intelligence driven 6G networks:endogenous intelligence
- Telecommunications Science Vol. 36, Issue 9, Pages: 14-22(2020)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020266
  CLC： TN929.5
- Published Online：2020-09，
  
  Published：20 September 2020
- 稿件说明：
移动端阅览
Tong ZHANG, Yijing REN, Shi YAN, et al. Artificial intelligence driven 6G networks:endogenous intelligence[J]. Telecommunications science, 2020, 36(9): 14-22.
DOI：

Tong ZHANG, Yijing REN, Shi YAN, et al. Artificial intelligence driven 6G networks:endogenous intelligence[J]. Telecommunications science, 2020, 36(9): 14-22. DOI： 10.11959/j.issn.1000-0801.2020266.

摘要

为了满足智能适配人-机-物深度互联需求，第六代移动通信系统（6G）将基于全频谱、面向全场景、支撑全业务，其核心组成是智慧内生，也是驱动5G演进的趋势。探讨了人工智能驱动的6G智慧内生网络的特征与组成，描述了智慧内生网络的关键技术和产业应用现状，介绍了国内外相关进展和标准化工作，也对未来的挑战进行了展望。

Abstract

In order to meetthe requirements of intelligently adapting to the needs of human-machine-thing deep interconnection

the sixth-generation mobile communication system (6G) will be based on full spectrum

face all scenarios and support all services.Endogenous intelligence is the core component of 6G and an inevitable trend driving 5G evolution.The characteristics and compositions of artificial intelligence driven 6G endogenous intelligent networks were discussed.The key technologies of endogenous intelligent network and current status of industrial applications were also described.Further

domestic and foreign related progress and standardization works were introduced

and also position future challenges were prospected.

关键词

Keywords

references

彭木根 , 孙耀华 , 王文博 . 智简6G无线接入网:架构、技术和展望 [Z ] . 2020 .

PENG M G , SUN Y H , WANG W B . Intelligent-concise radio access networks in 6G:architecture,techniques and insight [Z ] . 2020 .

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 .

李子姝 , 谢人超 , 孙礼 , 等 . 移动边缘计算综述 [J ] . 电信科学 , 2018 , 34 ( 1 ): 87 - 101 .

LI Z S , XIE R C , SUN L , et al . A survey of mobile edge computing [J ] . Telecommunications Science , 2018 , 34 ( 1 ): 87 - 101 .

CHEN S , LIANG Y , SUN S , et al . Vision,requirements,and technology trend of 6G:how to tackle the challenges of system coverage,capacity,user data-rate and movement speed [J ] . IEEE Wireless Communications , 2020 , 27 ( 2 ): 218 - 228 .

BORGERDING M , SCHNITER P , RANGAN S . AMP-inspired deep networks for sparse linear inverse problems [J ] . IEEE Transactions on Signal Processing , 2017 , 65 ( 16 ): 4293 - 4308 .

BAI Q , WANG J , ZHANG Y , et al . Deep learning-based channel estimation algorithm over time selective fading channels [J ] . IEEE Transactions on Cognitive Communications and Networking , 2020 , 6 ( 1 ): 125 - 134 .

YE H , LI G Y , JUANG B . Power of deep learning for channel estimation and signal detection in OFDM systems [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 1 ): 114 - 117 .

SUN Y , PENG M G , ZHOU Y , et al . Application of machine learning in wireless networks:key techniques and open issues [J ] . IEEE Communications Surveys ＆ Tutorials , 2019 , 21 ( 4 ): 3072 - 3108 .

SUN Y , PENG M G , POOR H V . A distributed approach to improving spectral efficiency in uplink device-to- device-enabled cloud radio access networks [J ] . IEEE Transactions on Communications , 2018 , 66 ( 12 ): 6511 - 6526 .

项弘禹 , 肖扬文 , 张贤 , 等 . 5G边缘计算和网络切片技术 [J ] . 电信科学 , 2017 , 33 ( 6 ): 54 - 63 .

XIANG H Y , XIAO Y W , ZHANG X , et al . Edge computing and network slicing technology in 5G [J ] . Telecommunications Science , 2017 , 33 ( 6 ): 54 - 63 .

CHEN X , ZHANG H , WU C , et al . Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 3 ): 4005 - 4018 .

ZHOU Y , PENG M G , YAN S , et al . Deep reinforcement learning based coded caching scheme in fog radio access networks [C ] // Proceedings of 2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops) . Piscataway:IEEE Press , 2018 : 309 - 313 .

DOAN K N , VAN NGUYEN T , QUEK T Q S , et al . Content-aware proactive caching for backhaul offloading in cellular network [J ] . IEEE Transactions on Wireless Communications , 2018 , 17 ( 5 ): 3128 - 3140 .

SUN Y , PENG M G . Joint cache and radio resource management in fog radio access networks:a hierarchical two-timescale optimization perspective [C ] // Proceedings of 2019 IEEE 30th Annual International Symposium on Personal,Indoor and Mobile Radio Communications (PIMRC) . Piscataway:IEEE Press , 2019 : 1 - 6 .

程日涛 , 张海涛 , 王乐 . 5G 无线网部署策略 [J ] . 电信科学 , 2018 , 34 ( Z1 ): 1 - 8 .

CHENG R T , ZHANG H T , WANG L . Deployment strategy of 5G wireless network [J ] . Telecommunications Science , 2018 , 34 ( Z1 ): 1 - 8 .

YAN S , JIAO M , ZHOU Y , et al . Machine learning approach for user association and content placement in fog radio access networks [J ] . IEEE Internet of Things Journal , 2020 ( 99 ):1.

SAMARAKOON S , BENNIS M , SAAD W , et al . Dynamic clustering and on/off strategies for wireless small cell networks [J ] . IEEE Transactions on Wireless Communications , 2016 , 15 ( 3 ): 2164 - 2178 .

SRINIVASAN S M , TRUONG-HUU T , GURUSAMY M . Machine learning-based link fault identification and localization in complex networks [J ] . IEEE Internet of Things Journal , 2019 , 6 ( 4 ): 6556 - 6566 .

WU W , PENG M , CHEN W , et al . Unsupervised deep transfer learning for fault diagnosis in fog radio access networks [J ] . IEEE Internet of Things Journal , 2020 PP ( 99 ):1.

曹汐 , 余立 , 马键 , 等 . 移动通信网络智能化分级评估方法研究 [C ] // 5G网络创新研讨会（2019）论文集 , 2019 : 332 - 338 .

CAO X , YU L , MA J , et al . Research on intelligent classification evaluation method of mobile communication network [C ] // Proceedings of 5G Network Innovation Seminar(2019) , 2019 : 332 - 338 .

Views

2237

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on service-driven network technologies for space-air-ground-sea integration

Smart fog radio access networks for 6G:architecture and key technologies

Homocentric sphere model: a novel network architecture supporting 6G dense deployment and high-speed mobility scenarios

Cloud-based intelligent computing center ten-thousand card cluster innovation and practice

Research on the evolution, key technologies, and applications for AI enabled 5G-Advanced core network running

Related Author

FAN Kexin

AN Lirong

ZHANG Qinyu

Xian ZHANG

Xueyan CAO

Binghong LIU

Yuan AI

Chenxi LIU

Related Institution

Harbin Institute of Technology

China Information and Communication Technology Mobile Communication Technology Co., Ltd.

State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications

State Key Laboratory of Wireless Mobile Communication, China Information and Communication Technologies Group Co., Ltd.

China Mobile Group Design Institute Co., Ltd.

⁰