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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.2
- 수록면
- 265 - 268 (4page)
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초록· 키워드
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With the rapid growth of computer vision applications, a large amount of video data in the Internet of Vehicles scenario are used for content analysis. Tasks based on video content understanding are usually accompanied by huge amount of calculation, which put great pressure on traditional wireless communication resource and Mobile Edge Computing (MEC) server computing resource. Furthermore, existing resource allocation schemes based on Quality of Service (QoS) or Quality of Experience (QoE) may not be the best choice for the purpose of video content understanding. In this paper, we propose a joint resource allocation scheme based on Quality of Content (QoC) to maximize the accuracy of video content understanding. Due to the real-time nature of resource allocation and the variability of the environment in autonomous driving scenarios, we design a Multi-agent Distributed Q-Learning algorithm to solve such multi-constrained nonlinear programming problems. Finally, the simulation results show that our proposed QoC-based joint resource allocation scheme has better video content understanding performance.
목차
Abstract
I. INTRODUCTION
II. SYSTEM MODEL AND PROBLEM FORMULATION
III. PROBLEM SOLUTION
IV. SIMULATION RESULTS
V. CONCLUSION
REFERENCES
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