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Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations

The 6G wireless systems are expected to have higher capacity, reliability, and energy efficiency than the existing cellular systems. Millimeter-wave (mmWave) frequencies offer high capacity at the cost of high attenuation and blockage losses. Reconfigurable intelligent surface (RIS) assisted mmWave...

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Autores principales: Ramamoorthi, Yoghitha, Ohmiya, Riku, Iwabuchi, Masashi, Ogawa, Tomoaki, Takatori, Yasushi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370948/
https://www.ncbi.nlm.nih.gov/pubmed/35957172
http://dx.doi.org/10.3390/s22155619
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author Ramamoorthi, Yoghitha
Ohmiya, Riku
Iwabuchi, Masashi
Ogawa, Tomoaki
Takatori, Yasushi
author_facet Ramamoorthi, Yoghitha
Ohmiya, Riku
Iwabuchi, Masashi
Ogawa, Tomoaki
Takatori, Yasushi
author_sort Ramamoorthi, Yoghitha
collection PubMed
description The 6G wireless systems are expected to have higher capacity, reliability, and energy efficiency than the existing cellular systems. Millimeter-wave (mmWave) frequencies offer high capacity at the cost of high attenuation and blockage losses. Reconfigurable intelligent surface (RIS) assisted mmWave networks consist of smaller antenna elements that control the propagation channel between the base station (BS) and the user by appropriately tuning the phase and the reflection of the incident electromagnetic signal. The deployment of RIS is considered to be an energy efficient solution to improve the coverage of regions with high blocking probability. However, if every BS is associated with one or more dedicated RIS, then the density of RIS increases proportionally with the density of BSs. Hence in this work, we propose RIS sharing mechanisms where multiple BSs share one RIS. We formulate resource allocation of RIS sharing in terms of time and the RIS elements as an optimization problem, and we propose heuristics to solve both. Further, we present detailed simulation results to compare time and the element based RIS sharing methods for various scenarios with the benchmark and the RIS system without sharing. The proposed time and element based RIS sharing methods improve throughput upto [Formula: see text] and [Formula: see text] , respectively, compared to the RIS system without sharing in specific scenarios.
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spelling pubmed-93709482022-08-12 Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations Ramamoorthi, Yoghitha Ohmiya, Riku Iwabuchi, Masashi Ogawa, Tomoaki Takatori, Yasushi Sensors (Basel) Article The 6G wireless systems are expected to have higher capacity, reliability, and energy efficiency than the existing cellular systems. Millimeter-wave (mmWave) frequencies offer high capacity at the cost of high attenuation and blockage losses. Reconfigurable intelligent surface (RIS) assisted mmWave networks consist of smaller antenna elements that control the propagation channel between the base station (BS) and the user by appropriately tuning the phase and the reflection of the incident electromagnetic signal. The deployment of RIS is considered to be an energy efficient solution to improve the coverage of regions with high blocking probability. However, if every BS is associated with one or more dedicated RIS, then the density of RIS increases proportionally with the density of BSs. Hence in this work, we propose RIS sharing mechanisms where multiple BSs share one RIS. We formulate resource allocation of RIS sharing in terms of time and the RIS elements as an optimization problem, and we propose heuristics to solve both. Further, we present detailed simulation results to compare time and the element based RIS sharing methods for various scenarios with the benchmark and the RIS system without sharing. The proposed time and element based RIS sharing methods improve throughput upto [Formula: see text] and [Formula: see text] , respectively, compared to the RIS system without sharing in specific scenarios. MDPI 2022-07-27 /pmc/articles/PMC9370948/ /pubmed/35957172 http://dx.doi.org/10.3390/s22155619 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
Ramamoorthi, Yoghitha
Ohmiya, Riku
Iwabuchi, Masashi
Ogawa, Tomoaki
Takatori, Yasushi
Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations
title Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations
title_full Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations
title_fullStr Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations
title_full_unstemmed Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations
title_short Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations
title_sort resource allocation and sharing methodologies when reconfigurable intelligent surfaces meet multiple base stations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370948/
https://www.ncbi.nlm.nih.gov/pubmed/35957172
http://dx.doi.org/10.3390/s22155619
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