JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Resource Allocation Strategy of Internet of Vehicles Using Reinforcement Learning |
| 영문제목(English Title) |
Resource Allocation Strategy of Internet of Vehicles Using Reinforcement Learning |
| 저자(Author) |
Hongqi Xi
Huijuan Sun
|
| 원문수록처(Citation) |
VOL 18 NO. 03 PP. 0443 ~ 0456 (2022. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits. |
| 키워드(Keyword) |
DDQN Model
Internet of Vehicles
Markov Decision Model
Mobile Edge Computing
System Model
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| 파일첨부 |
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