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Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946866/ https://www.ncbi.nlm.nih.gov/pubmed/35327837 http://dx.doi.org/10.3390/e24030326 |
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author | Cao, Hailin Zhu, Wang Feng, Wenjuan Fan, Jin |
author_facet | Cao, Hailin Zhu, Wang Feng, Wenjuan Fan, Jin |
author_sort | Cao, Hailin |
collection | PubMed |
description | Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS’s beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT approach can achieve more than 95% of the performance provided by the upper bound with low complexity. |
format | Online Article Text |
id | pubmed-8946866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89468662022-03-25 Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications Cao, Hailin Zhu, Wang Feng, Wenjuan Fan, Jin Entropy (Basel) Article Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS’s beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT approach can achieve more than 95% of the performance provided by the upper bound with low complexity. MDPI 2022-02-24 /pmc/articles/PMC8946866/ /pubmed/35327837 http://dx.doi.org/10.3390/e24030326 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cao, Hailin Zhu, Wang Feng, Wenjuan Fan, Jin Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications |
title | Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications |
title_full | Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications |
title_fullStr | Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications |
title_full_unstemmed | Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications |
title_short | Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications |
title_sort | robust beamforming based on graph attention networks for irs-assisted satellite iot communications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946866/ https://www.ncbi.nlm.nih.gov/pubmed/35327837 http://dx.doi.org/10.3390/e24030326 |
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