Yang, Yang and Ma, Mulei and Wu, Hequan and Yu, Quan and Zhang, Ping and You, Xiaohu and Wu, Jianjun and Peng, Chenghui and Yum, Tak-Shing Peter and Shen, Sherman and Aghvami, A Hamid and Li, Geoffrey Y and Wang, Jiangzhou and Liu, Guangyi and Gao, Peng and Tang, Xiongyan and Cao, Chang and Thompson, John and Wong, Kat-Kit and Chen, Shanzhi and Debbah, Merouane and Dustdar, Schahram and Eliassen, Frank and Chen, Tao and Duan, Xiangyang and Sun, Shaohui and Tao, Xiaofeng and Zhang, Qinyu and Huang, Jianwei and Cui, Shuguang and Zhang, Wenjun and Li, Jie and Gao, Yue and Zhang, Honggang and Chen, Xu and Ge, Xiaohu and Xiao, Yong and Wang, Cheng-Xiang and Zhang, Zaichen and Ci, Song and Mao, Guoqiang and Li, Changle and Shao, Ziyu and Zhou, Yong and Liang, Junrui and Li, Kai and Wu, Liantao and Sun, Fanglei and Wang, Kunlun and Liu, Zening and Yang, Kun and Wang, Jun and Gao, Teng and Shu, Hongfeng (2023) 6G Network AI Architecture for Everyone-Centric Customized Services. IEEE Network, 37 (5). pp. 71-80. DOI https://doi.org/10.1109/mnet.124.2200241
Yang, Yang and Ma, Mulei and Wu, Hequan and Yu, Quan and Zhang, Ping and You, Xiaohu and Wu, Jianjun and Peng, Chenghui and Yum, Tak-Shing Peter and Shen, Sherman and Aghvami, A Hamid and Li, Geoffrey Y and Wang, Jiangzhou and Liu, Guangyi and Gao, Peng and Tang, Xiongyan and Cao, Chang and Thompson, John and Wong, Kat-Kit and Chen, Shanzhi and Debbah, Merouane and Dustdar, Schahram and Eliassen, Frank and Chen, Tao and Duan, Xiangyang and Sun, Shaohui and Tao, Xiaofeng and Zhang, Qinyu and Huang, Jianwei and Cui, Shuguang and Zhang, Wenjun and Li, Jie and Gao, Yue and Zhang, Honggang and Chen, Xu and Ge, Xiaohu and Xiao, Yong and Wang, Cheng-Xiang and Zhang, Zaichen and Ci, Song and Mao, Guoqiang and Li, Changle and Shao, Ziyu and Zhou, Yong and Liang, Junrui and Li, Kai and Wu, Liantao and Sun, Fanglei and Wang, Kunlun and Liu, Zening and Yang, Kun and Wang, Jun and Gao, Teng and Shu, Hongfeng (2023) 6G Network AI Architecture for Everyone-Centric Customized Services. IEEE Network, 37 (5). pp. 71-80. DOI https://doi.org/10.1109/mnet.124.2200241
Yang, Yang and Ma, Mulei and Wu, Hequan and Yu, Quan and Zhang, Ping and You, Xiaohu and Wu, Jianjun and Peng, Chenghui and Yum, Tak-Shing Peter and Shen, Sherman and Aghvami, A Hamid and Li, Geoffrey Y and Wang, Jiangzhou and Liu, Guangyi and Gao, Peng and Tang, Xiongyan and Cao, Chang and Thompson, John and Wong, Kat-Kit and Chen, Shanzhi and Debbah, Merouane and Dustdar, Schahram and Eliassen, Frank and Chen, Tao and Duan, Xiangyang and Sun, Shaohui and Tao, Xiaofeng and Zhang, Qinyu and Huang, Jianwei and Cui, Shuguang and Zhang, Wenjun and Li, Jie and Gao, Yue and Zhang, Honggang and Chen, Xu and Ge, Xiaohu and Xiao, Yong and Wang, Cheng-Xiang and Zhang, Zaichen and Ci, Song and Mao, Guoqiang and Li, Changle and Shao, Ziyu and Zhou, Yong and Liang, Junrui and Li, Kai and Wu, Liantao and Sun, Fanglei and Wang, Kunlun and Liu, Zening and Yang, Kun and Wang, Jun and Gao, Teng and Shu, Hongfeng (2023) 6G Network AI Architecture for Everyone-Centric Customized Services. IEEE Network, 37 (5). pp. 71-80. DOI https://doi.org/10.1109/mnet.124.2200241
Abstract
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to effectively address diverse user requirements and guarantee everyone's Quality of Experience (QoE) remains an open problem. The Sixth Generation (6G) mobile systems will solve this problem by utilizing heterogenous network resources and pervasive intelligence to support everyone-centric customized services anywhere and anytime. In this article, we first coin the concept of Service Requirement Zone (SRZ) on the user side to characterize and visualize the integrated service requirements and preferences of specific tasks of individual users. On the system side, we further introduce the concept of User Satisfaction Ratio (USR) to evaluate the system's overall service ability of satisfying a variety of tasks with different SRZs. Then, we propose a network Artificial Intelligence (AI) architecture with integrated network resources and pervasive AI capabilities for supporting customized services with guaranteed QoEs. Finally, extensive simulations show that the proposed network AI architecture can consistently offer a higher USR performance than the cloud AI and edge AI architectures with respect to different task scheduling algorithms, random service requirements, and dynamic network conditions.
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 05 Sep 2022 16:12 |
| Last Modified: | 08 Mar 2024 09:24 |
| URI: | http://repository.essex.ac.uk/id/eprint/33388 |
Available files
Filename: 20220714_NetworkAI_v39_NetMag.pdf