智简6G无线接入网:架构、技术和展望

doi:10.13190/j.jbupt.2020-079

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (3): 1-10.doi: 10.13190/j.jbupt.2020-079

• 综述 • 下一篇

智简6G无线接入网:架构、技术和展望

彭木根¹, 孙耀华^1,2, 王文博¹

1. 北京邮电大学信息与通信工程学院, 北京 100876;
2. 北京邮电大学网络与交换技术国家重点实验室, 北京 100876

收稿日期:2020-06-20 出版日期:2020-06-28 发布日期:2020-12-04
通讯作者: 孙耀华(1992-),男,助理教授,E-mail:sunyaohua@bupt.edu.cn. E-mail:sunyaohua@bupt.edu.cn
作者简介:彭木根(1978-),男,教授,博士生导师.
基金资助:
国家自然科学基金项目（61925101，61831002）；中央高校基本科研业务费专项资金项目（24820202020RC11）

Intelligent-Concise Radio Access Networks in 6G: Architecture, Techniques and Insight

PENG Mu-gen¹, SUN Yao-hua^1,2, WANG Wen-bo¹

1. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-06-20 Online:2020-06-28 Published:2020-12-04
Supported by:

摘要/Abstract

摘要： 移动通信系统每十年演进一代，为了更好地满足人机物智能高速、低时延和大连接的通信需求，特别是提升垂直行业的信息化和智能化水平，急需向智能内生的第6代移动通信系统（6G）演进.为了实现智能内生，提出了一种极智极简的无线接入网体系架构，探讨了相应的基础理论和关键技术，对未来的6G特色业务和应用进行了展望.

关键词: 第6代移动通信系统, 智能内生, 智简网络, 人工智能

Abstract: Mobile communication systems evolve every ten years, and to meet the performance requirements of high bit rate, low latency, and massive connections for the intelligent people-machine-things communications, the intelligent-endogenesis 6th generation of mobile communications system (6G) is urgent to be evolved. In order to fulfill the functions of intelligent-endogenesis, an extreme-intelligent and extreme-concise system architecture of radio access networks is proposed. The corresponding fundamental theories and key techniques are outlined, and the future particular services and applications in 6G are discussed as well.

Key words: the 6th generation of mobile communications system, intelligent-endogenesis, intelligent-concise network, artificial intelligence

中图分类号:

TN929.5

彭木根, 孙耀华, 王文博. 智简6G无线接入网:架构、技术和展望[J]. 北京邮电大学学报, 2020, 43(3): 1-10.

PENG Mu-gen, SUN Yao-hua, WANG Wen-bo. Intelligent-Concise Radio Access Networks in 6G: Architecture, Techniques and Insight[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 1-10.

参考文献

[1] 刘杨, 彭木根. 6G内生安全:体系结构与关键技术[J]. 电信科学, 2020, 36(1):11-20. Liu Yang, Peng Mugen. 6G endogenous security:architecture and key technologies[J]. Telecommunications Science, 2020, 36(1):11-20.
[2] Yin Bonan, Peng Mugen, Yan Shi, et al. Tradeoff between ergodic rate and delivery latency in fog radio access networks[J]. IEEE Transactions on Wireless Communications, 2020, 19(4):2240-2251.
[3] O'Shea T, Hoydis J. An introduction to deep learning for the physical layer[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(4):563-575.
[4] Wang Junbo, Wang Junyuan, Wu Yongpeng, et al. A machine learning framework for resource allocation assisted by cloud computing[J]. IEEE Network, 2018, 32(2):144-151.
[5] Zhao Zhongyuan, Feng Chenyuan, Yang H H, et al. Federated-learning-enabled intelligent fog radio access networks:fundamental theory, key techniques, and future trends[J]. IEEE Wireless Communications, 2020, 27(2):22-28.
[6] Sun Yaohua, Peng Mugen, Mao Shiwen. Deep reinforcement learning-based mode selection and resource management for green fog radio access networks[J]. IEEE Internet of Things Journal, 2019, 6(2):1960-1971.
[7] Sun Yaohua, Peng Mugen, Mao Shiwen. A game-theoretic approach to cache and radio resource management in fog radio access networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(10):10145-10159.
[8] Yan Shi, Qi Lin, Zhou Yangcheng, et al. Joint user access mode selection and content popularity prediction in non-orthogonal multiple access-based F-RANs[J]. IEEE Transactions on Communications, 2020, 68(1):654-666.
[9] Muñoz P, Barco R, Ruiz-Avilés J M, et al. Fuzzy rule-based reinforcement learning for load balancing techniques in enterprise LTE femtocells[J]. IEEE Transactions on Vehicular Technology, 2013, 62(5):1962-1973.
[10] Cao Gang, Lu Zhaoming, Wen Xiangming, et al. AIF:an artificial intelligence framework for smart wireless network management[J]. IEEE Communications Letters, 2018, 22(2):400-403.
[11] Asghar A, Farooq H, Imran A. Self-healing in emerging cellular networks:review, challenges, and research directions[J]. IEEE Communications Surveys & Tutorials, 2018, 20(3):1682-1709.
[12] Gómez-Andrades A, Muñoz P, Serrano I, et al. Automatic root cause analysis for LTE networks based on unsupervised techniques[J]. IEEE Transactions on Vehicular Technology, 2016, 65(4):2369-2386.

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智简6G无线接入网:架构、技术和展望

Intelligent-Concise Radio Access Networks in 6G: Architecture, Techniques and Insight

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