6G Network AI Architecture for Everyone-Centric Customized Services

• Published in: IEEE network 2023-09, Vol.37 (5), p.71-80
• Main Authors: Yang, Yang, Ma, Mulei, Wu, Hequan, Yu, Quan, You, Xiaohu, Wu, Jianjun, Peng, Chenghui, Yum, Tak-Shing Peter, Aghvami, A. Hamid, Li, Geoffrey Y., Wang, Jiangzhou, Liu, Guangyi, Gao, Peng, Tang, Xiongyan, Cao, Chang, Thompson, John, Wong, Kat-Kit, Chen, Shanzhi, Wang, Zhiqin, Debbah, Merouane, Dustdar, Schahram, Eliassen, Frank, Chen, Tao, Duan, Xiangyang, Sun, Shaohui, Tao, Xiaofeng, Zhang, Qinyu, Huang, Jianwei, Zhang, Wenjun, Li, Jie, Gao, Yue, Zhang, Honggang, Chen, Xu, Ge, Xiaohu, Xiao, Yong, Wang, Cheng-Xiang, Zhang, Zaichen, Ci, Song, Mao, Guoqiang, Li, Changle, Shao, Ziyu, Zhou, Yong, Liang, Junrui, Li, Kai, Wu, Liantao, Sun, Fanglei, Wang, Kunlun, Liu, Zening, Yang, Kun, Wang, Jun, Gao, Teng, Shu, Hongfeng
• Format: Article
• Language: English
• Subjects: 5G mobile communication; 6G mobile communication; Algorithms; Artificial intelligence; Cloud computing; Computer architecture; Customization; Energy efficiency; Mobile communication; Performance evaluation; Quality of experience; Scheduling algorithms; Service computing; Task analysis; Task scheduling; User experience; User requirements; User satisfaction
• ISSN: 0890-8044; 1558-156X
• DOI: 10.1109/MNET.124.2200241

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.