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Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge
Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of...
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/PMC8948803/ https://www.ncbi.nlm.nih.gov/pubmed/35336560 http://dx.doi.org/10.3390/s22062390 |
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author | Souto, Victoria Dala Pegorara Souza, Richard Demo Uchôa-Filho, Bartolomeu F. Li, Yonghui |
author_facet | Souto, Victoria Dala Pegorara Souza, Richard Demo Uchôa-Filho, Bartolomeu F. Li, Yonghui |
author_sort | Souto, Victoria Dala Pegorara |
collection | PubMed |
description | Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of reflecting elements at the IRS and its passive operation represent an important challenge in the acquisition of the instantaneous channel state information (I-CSI) of all links as it adds a very high overhead to the system and requires equipping the IRS with radio-frequency chains. To overcome this problem, a new approach is proposed in order to optimize beamforming at the BS and the phase shifts at the IRS without considering any knowledge of I-CSI but while only exploring the statistical channel state information (S-CSI). We aim at maximizing the user-achievable rate subject to a maximum transmit power constraint. To achieve this goal, we propose a new two-phase framework. In the first phase, both the beamforming at the BS and IRS are designed based only on S-CSI and, in the second phase, the previously designed beamforming pair is used as an initial solution, and beamforming at the BS and IRS is designed only by considering the feedback of the SNR at UE. Moreover, for each phase, we propose new methods based on Genetic Algorithms. Results show that the developed algorithms can approach beamforming with I-CSI but with significantly reduced channel estimation overhead. |
format | Online Article Text |
id | pubmed-8948803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89488032022-03-26 Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge Souto, Victoria Dala Pegorara Souza, Richard Demo Uchôa-Filho, Bartolomeu F. Li, Yonghui Sensors (Basel) Article Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of reflecting elements at the IRS and its passive operation represent an important challenge in the acquisition of the instantaneous channel state information (I-CSI) of all links as it adds a very high overhead to the system and requires equipping the IRS with radio-frequency chains. To overcome this problem, a new approach is proposed in order to optimize beamforming at the BS and the phase shifts at the IRS without considering any knowledge of I-CSI but while only exploring the statistical channel state information (S-CSI). We aim at maximizing the user-achievable rate subject to a maximum transmit power constraint. To achieve this goal, we propose a new two-phase framework. In the first phase, both the beamforming at the BS and IRS are designed based only on S-CSI and, in the second phase, the previously designed beamforming pair is used as an initial solution, and beamforming at the BS and IRS is designed only by considering the feedback of the SNR at UE. Moreover, for each phase, we propose new methods based on Genetic Algorithms. Results show that the developed algorithms can approach beamforming with I-CSI but with significantly reduced channel estimation overhead. MDPI 2022-03-20 /pmc/articles/PMC8948803/ /pubmed/35336560 http://dx.doi.org/10.3390/s22062390 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 Souto, Victoria Dala Pegorara Souza, Richard Demo Uchôa-Filho, Bartolomeu F. Li, Yonghui Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge |
title | Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge |
title_full | Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge |
title_fullStr | Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge |
title_full_unstemmed | Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge |
title_short | Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge |
title_sort | intelligent reflecting surfaces beamforming optimization with statistical channel knowledge |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948803/ https://www.ncbi.nlm.nih.gov/pubmed/35336560 http://dx.doi.org/10.3390/s22062390 |
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