@inproceedings{6b3feaadf46148ae83ef45e83d8895eb,
title = "Efficient Resource Management Based on DQN in LEO Satellite Edge Computing System",
abstract = "In this paper, a low earth orbit (LEO) satellite edge computing architecture is proposed by placing the mobile edge computing (MEC) servers on the LEO satellites to provide computing services for ground users. To solve the offloading decision and resource allocation problems in multi-user, multi-LEO satellite and multi-task scenario, we propose a computation offloading algorithm based on deep reinforcement learning (DRL), derive the sub-optimal power allocation scheme, and obtain the optimal MEC resource allocation scheme through convex optimization algorithm. Simulation results show that the proposed algorithm achieves excellent convergence effect and system performance.",
keywords = "DRL, LEO satellite, convex optimization, edge computing",
author = "Jian Wu and Min Jia and Qing Guo and Xuemai Gu",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Globecom Workshops, GLOBECOM Workshop 2023 ; Conference date: 04-12-2023 Through 08-12-2023",
year = "2023",
doi = "10.1109/GCWkshps58843.2023.10464404",
language = "英语",
series = "2023 IEEE Globecom Workshops, GC Wkshps 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "135--140",
booktitle = "2023 IEEE Globecom Workshops, GC Wkshps 2023",
address = "美国",
}