Toward Native Intelligence: An Efficient and Flexible AI Services Provision Scheme in Multilayer Heterogeneous Networks

作     者：He, Jingchao Cheng, Nan Sun, Ruijin Zhang, Ruqian Zhou, Conghao Quan, Wei Li, Changle 

作者机构：Xidian Univ State Key Lab ISN Xian 710071 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China China Mobile Res Inst AI & Intelligent Operat Ctr Xian 710071 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China 

出 版 物：《IEEE INTERNET OF THINGS JOURNAL》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第9期

页      面：11708-11721页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2020YFB1807700] Fundamental Research Funds for the Central Universities Innovation Fund of Xidian University [YJSJ24017] 

主　　题：Artificial intelligence Computer architecture Computational modeling Delays Resource management Heterogeneous networks 6G mobile communication Satellites Mobile handsets Biological system modeling Artificial intelligence (AI) model multilayer network service deployment user-centric service 

摘      要：To fulfill future diverse user requirements, 6G networks are envisioned to provide everyone-centric customized services ubiquitously and precisely. However, the diversity in user requirements and the heterogeneity in network resources challenge conventional network operators in network management and service provision. In this article, we investigate the artificial intelligence (AI) service provision in the multilayer heterogeneous network. To provide ubiquitous intelligence to users with different computing requirements, an intelligence-native network architecture is designed. Based on the proposed architecture and the AI model stitching mechanism, we formulate the joint AI provision and access selection problem as a mixed integer nonlinear programming (MINLP) problem to maximize the average user satisfaction value and user satisfaction rate. Then, a heuristic solution based on Dung Beetle algorithm is proposed to optimize the AI model selection, AI service deployment, user access, and stitching coefficient jointly. Extensive simulations are conducted to evaluate the performance of our proposed architecture and algorithm.