DOAJ Open Access 2024

Reinforcement learning based edge computing in B5G

Jiachen Yang Yiwen Sun Yutian Lei Zhuo Zhang Yang Li +2 lainnya

Abstrak

The development of communication technology will promote the application of Internet of Things, and Beyond 5G will become a new technology promoter. At the same time, Beyond 5G will become one of the important supports for the development of edge computing technology. This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing. Through trial and error learning of agent, the optimal spectrum and power can be determined for transmission without global information, so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure. The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link.

Topik & Kata Kunci

Information technology

Penulis (7)

Jiachen Yang

Yiwen Sun

Yutian Lei

Zhuo Zhang

Yang Li

Yongjun Bao

Zhihan Lv

Format Sitasi

APA MLA BibTeX

Yang, J., Sun, Y., Lei, Y., Zhang, Z., Li, Y., Bao, Y. et al. (2024). Reinforcement learning based edge computing in B5G. https://doi.org/10.1016/j.dcan.2022.03.008

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Informasi Jurnal

Tahun Terbit: 2024
Sumber Database: DOAJ
DOI: 10.1016/j.dcan.2022.03.008
Akses: Open Access ✓