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6G Network AI Architecture for Everyone-Centric Customized Services

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
Source :
IEEE Network; September 2023, Vol. 37 Issue: 5 p71-80, 10p
Publication Year :
2023

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.

Details

Language :
English
ISSN :
08908044 and 1558156X
Volume :
37
Issue :
5
Database :
Supplemental Index
Journal :
IEEE Network
Publication Type :
Periodical
Accession number :
ejs65650051
Full Text :
https://doi.org/10.1109/MNET.124.2200241