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Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System
This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038743/ https://www.ncbi.nlm.nih.gov/pubmed/33917326 http://dx.doi.org/10.3390/s21072550 |
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author | Maimaiti, Saidiwaerdi Chuai, Gang Gao, Weidong Zhang, Jinxi |
author_facet | Maimaiti, Saidiwaerdi Chuai, Gang Gao, Weidong Zhang, Jinxi |
author_sort | Maimaiti, Saidiwaerdi |
collection | PubMed |
description | This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio. |
format | Online Article Text |
id | pubmed-8038743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80387432021-04-12 Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System Maimaiti, Saidiwaerdi Chuai, Gang Gao, Weidong Zhang, Jinxi Sensors (Basel) Article This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio. MDPI 2021-04-06 /pmc/articles/PMC8038743/ /pubmed/33917326 http://dx.doi.org/10.3390/s21072550 Text en © 2021 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 Maimaiti, Saidiwaerdi Chuai, Gang Gao, Weidong Zhang, Jinxi Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System |
title | Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System |
title_full | Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System |
title_fullStr | Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System |
title_full_unstemmed | Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System |
title_short | Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System |
title_sort | beam allocation and power optimization for energy-efficiency in multiuser mmwave massive mimo system |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8038743/ https://www.ncbi.nlm.nih.gov/pubmed/33917326 http://dx.doi.org/10.3390/s21072550 |
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