Optimization of Quality of AI Service in 6G Native AI Wireless Networks

被引:4
作者
Chen, Tianjiao [1 ,2 ]
Deng, Juan [1 ,2 ]
Tang, Qinqin [3 ]
Liu, Guangyi [1 ,2 ]
机构
[1] China Mobile Res Inst, Beijing 100053, Peoples R China
[2] ZGC Inst Ubiquitous X Innovat & Applicat, Beijing 100088, Peoples R China
[3] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
关键词
native AI wireless networks; quality of AI service; task scheduling; resource allocation; CHALLENGES; IOT;
D O I
10.3390/electronics12153306
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To comply with the trend of ubiquitous intelligence in 6G, native AI wireless networks are proposed to orchestrate and control communication, computing, data, and AI model resources according to network status, and efficiently provide users with quality-guaranteed AI services. In addition to the quality of communication services, the quality of AI services (QoAISs) includes multiple dimensions, such as AI model accuracy, overhead, and data privacy. This paper proposes a QoAIS optimization method for AI training services in 6G native AI wireless networks. To improve the accuracy and reduce the delay of AI services, we formulate an integer programming problem to obtain proper task scheduling and resource allocation decisions. To quickly obtain decisions that meet the requirements of each dimension of QoAIS, we further transform the single-objective optimization problem into a multi-objective format to facilitate the QoAIS configuration of network protocols. Considering the computational complexity, we propose G-TSRA and NSG-TSRA heuristic algorithms to solve the proposed problems. Finally, the feasibility and performance of QoAIS optimization are verified by simulation.
引用
收藏
页数:17
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