Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Cao, Hailin, Zhu, Wang, Feng, Wenjuan, Fan, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784674293727100928
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
work_keys_str_mv AT caohailin robustbeamformingbasedongraphattentionnetworksforirsassistedsatelliteiotcommunications
AT zhuwang robustbeamformingbasedongraphattentionnetworksforirsassistedsatelliteiotcommunications
AT fengwenjuan robustbeamformingbasedongraphattentionnetworksforirsassistedsatelliteiotcommunications
AT fanjin robustbeamformingbasedongraphattentionnetworksforirsassistedsatelliteiotcommunications