Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving
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%.
Guangdong Basic and Applied Basic Research Foundation["2019B1515130003","2021B1515120008","2021B1515120067","2022A1515010109"] ; National Natural Science Foundation of China
|WOS Research Area|
Computer Science ; Engineering ; Telecommunications
Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
|WOS Accession No|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Electrical and Electronic Engineering|
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 Affilication||Department of Electrical and Electronic Engineering|
|First Author's First Affilication||Department of Electrical and Electronic Engineering|
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.
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.
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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