@inproceedings{9a349071ff5144fc950c197ff02ed084,
title = "Spectrum Situation Completion Based on Model-Enhanced Generative Learning",
abstract = "Three-dimensional (3D) spectrum situation can be exploited to address the problem of spectrum resource under-utilization in integrated space and terrestrial information networks. In order to improve the accuracy of 3D spectrum situation completion under limited unmanned aerial vehicle (UAV) trajectory, this paper proposes a 3D spectrum situation completion scheme based on model-enhanced generative learning, which effectively solves the problem of unknown prior information of the complete spectrum situation and effectively digs the environmental characteristics of the 3D electromagnetic spectrum space. Furthermore, this paper proposes an improved generative adversarial networks structure and a series of data processing methods to reduce the scheme's completion error and training time. Simulation results demonstrate that the 3D spectrum situation completion scheme proposed in this paper can significantly outperform the conventional interpolation-based algorithm in terms of completion accuracy.",
keywords = "Spectrum situation completion, generative adversarial networks, generative learning, model-enhanced",
author = "Dong Liu and Yang Huang and Zhen Gao",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th International Conference on Wireless Communications and Signal Processing, WCSP 2021 ; Conference date: 20-10-2021 Through 22-10-2021",
year = "2021",
doi = "10.1109/WCSP52459.2021.9613512",
language = "English",
series = "13th International Conference on Wireless Communications and Signal Processing, WCSP 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "13th International Conference on Wireless Communications and Signal Processing, WCSP 2021",
address = "United States",
}