中文版 | English
Title

Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving

Author
Publication Years
2023
DOI
Source Title
ISSN
2162-2337
EISSN
2162-2345
VolumePPIssue:99Pages:1-1
Abstract
Edge intelligence autonomous driving (EIAD) offers computing resources in autonomous vehicles for training deep neural networks. However, wireless channels between the edge server and the autonomous vehicles are time-varying due to the high-mobility of vehicles. Moreover, the required number of training samples for different data modalities, e.g., images, point-clouds, is diverse. Consequently, when collecting these datasets from vehicles to the edge server, the associated bandwidth and power allocation across all data frames is a large-scale multi-modal optimization problem. This article proposes a highly computationally efficient algorithm that directly maximizes the quality of training (QoT). The key ingredients include a data-driven model for quantifying the priority of data modality and two first-order methods termed accelerated gradient projection and dual decomposition for low-complexity resource allocation. Finally, high-fidelity simulations in Car Learning to Act (CARLA) show that the proposed algorithm reduces the perception error by 3% and the computation time by 98%.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
First
Scopus EID
2-s2.0-85151572965
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10084349
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/524260
DepartmentDepartment of Electrical and Electronic Engineering
Affiliation
1.Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
2.College of Information Science and Technology, Jinan University, Guangzhou, China
3.Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
4.Shenzhen Research Institute of Big Data, Shenzhen, China
5.School of Science and Engineering, The Chinese University of Hong Kong Shenzhen and Shenzhen Research Institute of Big Data, China
First Author AffilicationDepartment of Electrical and Electronic Engineering
First Author's First AffilicationDepartment of Electrical and Electronic Engineering
Recommended Citation
GB/T 7714
Li,Xinrao,Zhang,Tong,Wang,Shuai,et al. Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving[J]. IEEE Wireless Communications Letters,2023,PP(99):1-1.
APA
Li,Xinrao,Zhang,Tong,Wang,Shuai,Zhu,Guangxu,Wang,Rui,&Chang,Tsung Hui.(2023).Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving.IEEE Wireless Communications Letters,PP(99),1-1.
MLA
Li,Xinrao,et al."Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving".IEEE Wireless Communications Letters PP.99(2023):1-1.
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