Joint Communication and Computing Resource Optimization for Collaborative AI Inference in Mobile Networks

被引:1
作者
Li, Nan [1 ,2 ]
Li, Xiang [2 ]
Yan, Yiwei [2 ]
Sun, Qi [2 ]
Han, Yantao [3 ]
Cheng, Kun [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Beijing, Peoples R China
[2] China Mobile Res Inst, Beijing, Peoples R China
[3] China Mobile Commun Grp Co, Technol Dept, Beijing, Peoples R China
来源
2023 IEEE 98TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-FALL | 2023年
关键词
AI Inference; Computing Collaboration; Communication and Computing Integration; RAN; 5G; 6G;
D O I
10.1109/VTC2023-Fall60731.2023.10333702
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Integrated AI and Communication have been identified as crucial elements for IMT2030 by ITU-R. The integration of communication and computing in the RAN will facilitate deploying AI technology closer to mobile devices and enable AI computing collaboration with reduced latency. This paper primarily investigates the collaboration of AI inference computation between mobile devices and base stations, with a specific focus on the joint optimization of communication and computing resources in a multi-user environment. A communication and computing integrated scheduling algorithm is proposed to ensure optimal system performance in terms of inference accuracy and computation latency. Simulation results validate the effectiveness of the algorithm, demonstrating superior performance compared to the baseline algorithm.
引用
收藏
页数:5
相关论文
共 16 条
[1]  
[Anonymous], 2021, 3GPP TR 22.874 v18.2.0 3rd Generation Partnership Project
[2]  
[Anonymous], 2022, China mobile. end side computing force network white paper
[3]   vrAIn: A Deep Learning Approach Tailoring Computing and Radio Resources in Virtualized RANs [J].
Ayala-Romero, Jose A. ;
Garcia-Saavedra, Andres ;
Gramaglia, Marco ;
Costa-Perez, Xavier ;
Banchs, Albert ;
Alcaraz, Juan J. .
MOBICOM'19: PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING, 2019,
[4]  
ITU-R, 2023, Framework and Overall Objectives of the Future Development of IMT for 2030 and Beyond
[5]   Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [J].
Letaief, Khaled B. ;
Shi, Yuanming ;
Lu, Jianmin ;
Lu, Jianhua .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2022, 40 (01) :5-36
[6]   Towards the Deep Convergence of Communication and Computing in RAN: Scenarios, Architecture, Key Technologies, Challenges and Future Trends [J].
Li, Nan ;
Sun, Qi ;
Li, Xiang ;
Guo, Fengxian ;
Huang, Yuhong ;
Chen, Ziqi ;
Yan, Yiwei ;
Peng, Mugen .
CHINA COMMUNICATIONS, 2023, 20 (03) :218-235
[7]   Resource Allocation and Task Scheduling Scheme in Priority-Based Hierarchical Edge Computing System [J].
Liao, Jing Xian ;
Wu, Xue Wen .
2020 19TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS ENGINEERING AND SCIENCE (DCABES 2020), 2020, :46-49
[8]  
Lin S., 2022, arXiv
[9]   Jellyfish: Timely Inference Serving for Dynamic Edge Networks [J].
Nigade, Vinod ;
Bauszat, Pablo ;
Bal, Henri ;
Wang, Lin .
2022 IEEE 43RD REAL-TIME SYSTEMS SYMPOSIUM (RTSS 2022), 2022, :277-290
[10]  
Peng C., 2022, Radio communications technologies, V48, P9