@inproceedings{4731eed9a99446beb37b354cb304bbe2,
title = "Topology Planning for LEO Satellites Based on Deep Reinforcement Learning",
abstract = "The Low Earth Orbit (LEO) satellite network has made significant advancements in global communication services, offering low latency and extensive coverage. Effective topology planning has the dual benefit of enhancing network Quality of Service and reducing signal delay, while simultaneously optimizing resource utilization. The advent of deep reinforcement learning (DRL) methodologies offers novel approaches to inter-satellite topology planning in LEO constellations. In this work, a DRL topology planning algorithm based minimum spanning tree (DQN-MST) is proposed and compared with two traditional graph optimization algorithms. The results demonstrate that DQN-MST reduces the total network link length by 38.20% and 62.62% compared to the graph optimization algorithms, confirming the effectiveness of DQN-MST.",
keywords = "deep reinforcement learning (DRL), inter-satellite, Low Earth Orbit (LEO), topology planning",
author = "Chang He and Wei Liu and Zhengbi Yong and Zhiyuan Xu and Qihao Zhou and Shilei Li and Dawei Shi",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024 ; Conference date: 08-12-2024 Through 10-12-2024",
year = "2024",
doi = "10.1109/ONCON62778.2024.10931301",
language = "English",
series = "2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference, ONCON 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 IEEE 3rd Industrial Electronics Society Annual On-Line Conference, ONCON 2024",
address = "United States",
}