Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

As one of the key technologies in the fifth generation of mobile communications, massive multi-input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only...

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Autores principales:	Jing Yang, Liping Zhang, Chunhua Zhu, Xinying Guo, Jiankang Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology T Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/074ca12930a042d1bbf0fd02fada8836
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spelling	oai:doaj.org-article:074ca12930a042d1bbf0fd02fada88362021-11-22T01:10:19ZEnergy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm1530-867710.1155/2021/6622830https://doaj.org/article/074ca12930a042d1bbf0fd02fada88362021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6622830https://doaj.org/toc/1530-8677As one of the key technologies in the fifth generation of mobile communications, massive multi-input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.Jing YangLiping ZhangChunhua ZhuXinying GuoJiankang ZhangHindawi-WileyarticleTechnologyTTelecommunicationTK5101-6720ENWireless Communications and Mobile Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Telecommunication TK5101-6720
spellingShingle	Technology T Telecommunication TK5101-6720 Jing Yang Liping Zhang Chunhua Zhu Xinying Guo Jiankang Zhang Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm
description	As one of the key technologies in the fifth generation of mobile communications, massive multi-input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.
format	article
author	Jing Yang Liping Zhang Chunhua Zhu Xinying Guo Jiankang Zhang
author_facet	Jing Yang Liping Zhang Chunhua Zhu Xinying Guo Jiankang Zhang
author_sort	Jing Yang
title	Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm
title_short	Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm
title_full	Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm
title_fullStr	Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm
title_full_unstemmed	Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm
title_sort	energy efficiency optimization of massive mimo systems based on the particle swarm optimization algorithm
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/074ca12930a042d1bbf0fd02fada8836
work_keys_str_mv	AT jingyang energyefficiencyoptimizationofmassivemimosystemsbasedontheparticleswarmoptimizationalgorithm AT lipingzhang energyefficiencyoptimizationofmassivemimosystemsbasedontheparticleswarmoptimizationalgorithm AT chunhuazhu energyefficiencyoptimizationofmassivemimosystemsbasedontheparticleswarmoptimizationalgorithm AT xinyingguo energyefficiencyoptimizationofmassivemimosystemsbasedontheparticleswarmoptimizationalgorithm AT jiankangzhang energyefficiencyoptimizationofmassivemimosystemsbasedontheparticleswarmoptimizationalgorithm
_version_	1718418321709203456

Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

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