6G Network AI Architecture for Everyone-Centric Customized Services

by   Yang Yang, et al.

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.


page 1

page 2

page 3

page 4


Slicing-Based AI Service Provisioning on Network Edge

Edge intelligence leverages computing resources on network edge to provi...

Integrated Sensing-Communication-Computation for Edge Artificial Intelligence

Edge artificial intelligence (AI) has been a promising solution towards ...

AI-Native Network Slicing for 6G Networks

With the global roll-out of the fifth generation (5G) networks, it is ne...

Holistic Network Virtualization and Pervasive Network Intelligence for 6G

In this tutorial paper, we look into the evolution and prospect of netwo...

A general-purpose AI assistant embedded in an open-source radiology information system

Radiology AI models have made significant progress in near-human perform...

User Intention Recognition and Requirement Elicitation Method for Conversational AI Services

In recent years, chat-bot has become a new type of intelligent terminal ...

Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI...

Please sign up or login with your details

Forgot password? Click here to reset