The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (1): 27-40.doi: 10.19682/j.cnki.1005-8885.2022.2004
• Special Topic: Intellicise Communication System • Previous Articles Next Articles
Liu Guangyi, Deng Juan, Zheng Qingbi, Li Gang, Sun Xin, Huang Yuhong
Received:
2021-12-13
Revised:
2022-01-19
Accepted:
2022-01-25
Online:
2022-02-26
Published:
2022-02-28
Contact:
Corresponding author: Zheng Qingbi
E-mail:zhengqingbi@chinamobile.com
Supported by:
CLC Number:
Liu Guangyi, Deng Juan, Zheng Qingbi, Li Gang, Sun Xin, Huang Yuhong. Native intelligence for 6G mobile network: technical challenges, architecture and key features[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(1): 27-40.
Add to citation manager EndNote|Ris|BibTeX
URL: https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2022.2004
References [1] LIU G Y, JIN J, WANG Q X, et al. 6G vision and demand: Digital twin and intelligent ubiquitous. Mobile Communications, 2020, 44(6): 3 - 9 (in Chinese). [2] ITU-T Y. 3173—2020. Framework for evaluating intelligence levels of future networks including IMT-2020 (Study Group 13). [3] 3GPP TS 37. 816 Version 1. 0. 0. Study on RAN-centric data collection and utilization for LTE and NR. 2019. [4] 3GPP TR 23. 791. Study of enablers for network automation for 5G. 2018. [5] 3GPP TSG-SA SP-190785. New WID self-organizing networks (SON) for 5G networks. 2021. [6] ASHMORE R, CALINESCU R, PATERSON C. Assuring the machine learning lifecycle: Desiderata, methods, and challenges. ACM Computing Surveys, 2021, 54(5): Article 111. [7] LETAIEF K B, CHEN W, SHI Y M, et al. The roadmap to 6G: AI empowered wireless networks. IEEE Communications Magazine, 2019, 57(8): 84 - 90. [8] SHAFIN R, LIU L J, CHANDRASEKHAR V, et al. Artificial intelligence-enabled cellular networks: A critical path to beyond-5G and 6G. IEEE Wireless Communications, 2020, 27(2): 212 - 217. [9] KATO N, MAO B M, TANG F X, et al. Ten challenges in advancing machine learning technologies toward 6G. IEEE Wireless Communications, 2020, 27(3): 96 - 103. [10] LIN M T, ZHAO Y P. Artificial intelligence-empowered resource management for future wireless communications: A survey. China Communications, 2020, 17(3): 66 - 85. [11] VAZQUEZ M N, PALLOIS J P, DEBBAH M, et al. Deploying artificial intelligence in the wireless infrastructure: The challenges ahead. Proceeding of the IEEE 2nd 5G World Forum (5GWF'19), 2019, Sept 30 - Oct 2, Dresden, Germany. Piscataway, NJ, USA: IEEE, 2019. [12] LEE G M, UM T W, CHOI J K. AI as a microservice (AIMS) over 5G network. Proceeding of the 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K'18), 2018, Nov 26 - 28, Santa Fe, Argentina. Piscataway, NJ, USA: IEEE, 2018. [13] From cloud AI to network AI: A view from 6GANA. 6GANA (6G Alliance of Network AI). 2021. [14] OUYANG Y, WANG L L, YANG A D, et al. The next decade of telecommunications artificial intelligence. ArXiv: 2101. 091632021. [15] Improved operator experience through Experiential Networked Intelligence (ENI). ETSI White Paper No 22. 2017. [16] Study on applying AI in telecommunication network. CCSA TC1-WG1#58 meeting, 2017. [17] 3GPP RAN3 SI. RAN-centric data collection and utilization. 2018. [18] 3GPP. 3GPP TR28. 805 V1. 1. 0. Study on management aspects of communication services. 2019. [19] ITU-T Y. 3172—2019. Architectural framework for machine learning in future networks including IMT-2020 (Study Group 13). [20] AI in Network use cases in China. White paper. GSMA, 2019. [21] 3GPP. 3GPP TR28. 810 V17. 0. 0. Study on concept, requirements and solutions for levels of autonomous network (Release 17). [22] FG ML5G Technical Specification "Requirements, architecture, and design for machine learning function orchestrator". ITU-T SG13-TD578 / WP1. [23] FG ML5G Technical Specification "Machine learning sandbox for future networks including IMT-2020: Requirements and architecture framework". ITU-T SG13-TD575 / WP1. [24] LIU G Y, HUANG Y H, LI N, et al. Vision, requirements and network architecture of 6G mobile network beyond 2030. China Communications, 2020, 17(9): 92 - 104. [25] AMIRI M M, GUNDUZ D. Federated learning over wireless fading channels. IEEE Transactions on Wireless Communications, 2020, 19(5): 3546 - 3557. [26] HUANG Y P, CHENG Y L, BAPNA A, et al. GPipe: Efficient training of giant neural networks using pipeline parallelism. Proceedings of the 33rd International Conference on Neural Information Processing Systems (NIPS'18), 2018, Dec 8 - 14, Vancouver, Canada. Red Hook, NY, USA: Curran Associates Inc, 2018: 1 - 10. [27] TEERAPITTAYANON S, MCDANEL B, KUNG H T. Distributed deep neural networks over the cloud, the edge and end devices. Proceeding of the IEEE 37th International Conference on Distributed Computing Systems (ICDCS'17). 2017, Jun 5 - 8, Atlanta, GA, USA. Piscataway, NJ, USA: IEEE, 2017. [28] WANG S Q, TUOR T, SALONIDIS T, et al. Adaptive federated learning in resource constrained edge computing systems. IEEE Journal on Selected Areas in Communications, 2019, 37(6): 1205 - 1221. [29] DUNNER C, LUCCHI A, GARGIANI M, et al. A distributed second-order algorithm you can trust. Proceeding of the 35th International Conference on Machine Learning (ICML'18), 2018, Jul 10 - 15, Stockholm, Schweden. Piscataway, NJ, USA: IEEE, 2018: 1358 - 1366. [30] ITU-T SG 13. Draft new recommendation ITU-T Y. DTN-ReqArch: "Requirements and architecture of digital twin network". 2020. [31] DENG J, ZHENG Q B, LIU G Y, et al. A digital twin approach for self-optimization of mobile networks. 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2021, March 29, Nanjing, China. Piscataway, NJ, USA: IEEE, 2021: 1 - 6. [32] LIU G Y, LI N, DENG J, et al. The SOLIDS 6G mobile network architecture: Driving forces, features, and functional topology. Engineering, 2021, DOI: 10.1016/j.eng.2021.07.013. |
[1] | Zhang Ping, Xu Xiaodong, Dong Chen, Han Shujun, Wang Bizhu. Intellicise communication system: model-driven semantic communications [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(1): 2-12. |
[2] | Sun Junshuai, Zhu Xinghui, Xiao Yeqiu, Cheng Ke, Zhao Shuangrui. Adaptive TTI bundling with self-healing scheme for 5G [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(1): 64-70. |
[3] | Guo Hui, Zhao Xuehui. Maximum throughput design for IRS aided WPCN system based on NOMA [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(1): 93-101. |
[4] | Liu Xu, Xie Yang. Channel estimation for multi-panel millimeter wave MIMO based on joint compressed sensing [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(6): 1-7. |
[5] | Zheng Lin, Wang Zhen, Chen Jianmei, Lin Mengying, Deng Xiaofang. MIMO-FSK non-coherent detection with spatial multiplexing in fast-fading environment [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(5): 47-54. |
[6] | Zhang Yongchang. Game-Based Distributed Noncooperation Interference Coordination Scheme in Ultra-Dense Networks [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(5): 55-62. |
[7] | Li Xinmin, Li Guomin, Liu Yang, Guo Tian, Li Pu, Li Yaru. Low-complexity transmit antenna selection algorithm for massive MIMO [J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(5): 63-68. |
[8] | Gro Yanyan, Li Shuai, Wu Chao. QoS-based optimal and fair resource allocation for energy-efficiency uplink NOMA networks [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2020, 27(3): 83-92. |
[9] | Xing Shuchen, Wen Xiangming, Lu Zhaoming, Pan Qi, Jing Wenpeng. Performance analysis and enhancement of random access process in cellular-IoT [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2019, 26(6): 1-10. |
[10] |
Peng Weiping, Su Zhe, Song Cheng, Jia Zongpu.
Research on adaptive dual task offloading decision algorithm for parking space recommendation service
|
[11] | Xin LI Gang XIE Jin-chun GAO. TMAHS: a truthful multi-unit double auction framework for heterogeneous spectrum in secondary market [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2019, 26(1): 82-94. |
[12] | Qu Tuosi, Cao Haiyan, Xu Fangmin, Wang Xiumin. Low complexity detection algorithm based on optimized Neumann series for massive MIMO system [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2018, 25(6): 97-100. |
[13] | . Spectrum allocation for wireless backhaul in heterogeneous ultra-dense networks [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2018, 25(3): 24-32. |
[14] | Li Wanghong, Zhu Qi. Network selection algorithm based on AHP and similarity [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2018, 25(2): 77-88. |
[15] | . Low-profile microwave lens antenna based on isotropic Huygens’ metasurfaces [J]. JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM, 2017, 24(4): 10-15. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||