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.

翻译：移动通信标准一直致力于通过利用更多无线电资源、提升频谱效率和能量效率来增强传输与网络性能。然而，如何有效满足多样化用户需求、保障每位用户的体验质量（QoE）仍是一个开放性问题。第六代（6G）移动系统将通过利用异构网络资源和泛在智能，随时随地支持人人定制的个性化服务来解决这一问题。本文首先在用户侧提出"服务需求区域"（SRZ）概念，用于表征并可视化个体用户在特定任务中的综合服务需求与偏好；在系统侧进一步提出"用户满意度比"（USR）概念，用于评估系统在满足具有不同SRZ的多类任务时的整体服务能力。随后，我们提出一种集成网络资源与泛在AI能力的网络人工智能（AI）架构，以支持具有QoE保障的定制化服务。最后，大量仿真结果表明：在不同任务调度算法、随机服务需求和动态网络条件下，所提出的网络AI架构相比云AI架构和边缘AI架构，能够持续提供更高的USR性能。