6G网络AI架构，用于每个以每个以每个以定制服务为中心的服务

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6G网络AI架构，用于每个以每个以每个以定制服务为中心的服务

6G Network AI Architecture for Everyone-Centric Customized Services

论文作者

Yang, Yang, Ma, Mulei, Wu, Hequan, Yu, Quan, Zhang, Ping, You, Xiaohu, Wu, Jianjun, Peng, Chenghui, Yum, Tak-Shing Peter, Shen, Sherman, Aghvami, Hamid, Li, Geoffrey Y, Wang, Jiangzhou, Liu, Guangyi, Gao, Peng, Tang, Xiongyan, Cao, Chang, Thompson, John, Wong, Kat-Kit, Chen, Shanzhi, Debbah, Merouane, Dustdar, Schahram, Eliassen, Frank, Chen, Tao, Duan, Xiangyang, Sun, Shaohui, Tao, Xiaofeng, Zhang, Qinyu, Huang, Jianwei, Cui, Shuguang, 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

论文摘要

开发了移动通信标准，用于通过使用更多无线电资源并提高频谱和能源效率来增强传输和网络性能。如何有效满足各种用户需求并确保每个人的经验质量（QOE）仍然是一个开放的问题。第六代（6G）移动系统将利用异源网络资源和普遍的智能来解决此问题，以随时随地支持每个以每个以每个以每个以每个以每个人的定制服务的服务）来解决此问题。在本文中，我们首先在用户端将服务需求区域（SRZ）的概念构成，以表征和可视化单个用户特定任务的集成服务要求和偏好。在系统方面，我们进一步介绍了用户满意度比（USR）的概念，以评估系统的总体服务能力，即通过不同的SRZ满足各种任务。然后，我们提出了一个网络人工智能（AI）体系结构，具有集成的网络资源和Perrvasive AI功能，以通过保证的QoE支持定制服务。最后，广泛的模拟表明，与云AI和Edge AI架构相比，相对于不同的任务调度算法，随机服务要求和动态网络条件，拟议的网络AI体系结构可以始终如一地提供USR性能。

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.

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