@inproceedings{0ad9db0aa6464a8fb59fc6281b32166d,
title = "Topology Prediction Method for UAV Ad Hoc Network Based on ASTGCN",
abstract = "Unmanned Aerial Vehicle (UAV) network has excellent mobility and flexibility, and can efficiently perform various complex tasks. However, UAV are highly dynamic, which causes the network topology to change frequently. In order to solve the above problems, this paper presents a topology prediction method of UAV network based on the space-time attention mechanism graph convolution network. This method utilizes the historical state information of each node in the network to predict its future connectivity. Compared to conventional approaches, our proposed method demonstrates improved accuracy and stability.",
keywords = "GCN, UAV ad hoc network, attention mechanism, topology prediction",
author = "Yan Tang and Yuyao Shen and Yiming Liu and Zhifeng Ma",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 4th International Conference on Electronics, Circuits and Information Engineering, ECIE 2024 ; Conference date: 24-05-2024 Through 26-05-2024",
year = "2024",
doi = "10.1109/ECIE61885.2024.10626780",
language = "English",
series = "2024 4th International Conference on Electronics, Circuits and Information Engineering, ECIE 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "620--626",
booktitle = "2024 4th International Conference on Electronics, Circuits and Information Engineering, ECIE 2024",
address = "United States",
}