6G Network AI Architecture for Everyone-Centric Customized Services

TL;DR

This paper proposes a novel 6G AI network architecture that leverages heterogenous resources and pervasive intelligence to provide personalized, quality-guaranteed services for all users, addressing diverse requirements effectively.

Contribution

It introduces the concepts of Service Requirement Zone (SRZ) and User Satisfaction Ratio (USR) to characterize user needs and evaluate system performance, along with an integrated AI architecture for 6G networks.

Findings

The proposed architecture outperforms cloud and edge AI in USR metrics.

Simulations demonstrate robustness under various network conditions.

Supports diverse user requirements with higher satisfaction levels.

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.