On the Energy Efficiency of Multicell Massive MIMO with Antenna Selection and Power Allocation
The energy consumption of massive multiple-input multiple-output (MIMO) systems increases with the number of antennas. Optimizing the energy efficiency (EE) of massive MIMO systems is one of the ways to achieve green communication. This paper proposes an EE optimization method that genetic algorithm...
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| Veröffentlicht in: | Wireless communications and mobile computing 2022-04, Vol.2022, p.1-11 |
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| creator | Du, Liping Tan, Ying Li, Yiming Chen, Yueyun |
| description | The energy consumption of massive multiple-input multiple-output (MIMO) systems increases with the number of antennas. Optimizing the energy efficiency (EE) of massive MIMO systems is one of the ways to achieve green communication. This paper proposes an EE optimization method that genetic algorithm-based antenna selection and power allocation (GA-AS+PA) for the downlink of a multicell massive MIMO system under the restriction of the users’ sum-rate. First, we use the genetic algorithm to determine the active transmitting antenna of each base station (BS). Then, the transmission power for each user is allocated using the convex optimization method. Finally, the EE of system is optimized under the achieved optimum BS’s transmit power and the number of active antennas. From the simulation results, the GA-AS+PA method can improve the EE of the system while meeting user sum-rate requirements, which achieves better performance compared with random antenna selection+equal power allocation method (RAS+EPA), random antenna selection+power allocation method (RAS+PA), the antenna selection method based on genetic algorithm+equal power allocation method (GA-AS+EPA), and equal power allocation (EPA) these four methods. The EE of the proposed GA-AS+PA method is improved by 33.3% compared to the EPA method. |
| doi_str_mv | 10.1155/2022/7224731 |
| format | Article |
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Optimizing the energy efficiency (EE) of massive MIMO systems is one of the ways to achieve green communication. This paper proposes an EE optimization method that genetic algorithm-based antenna selection and power allocation (GA-AS+PA) for the downlink of a multicell massive MIMO system under the restriction of the users’ sum-rate. First, we use the genetic algorithm to determine the active transmitting antenna of each base station (BS). Then, the transmission power for each user is allocated using the convex optimization method. Finally, the EE of system is optimized under the achieved optimum BS’s transmit power and the number of active antennas. From the simulation results, the GA-AS+PA method can improve the EE of the system while meeting user sum-rate requirements, which achieves better performance compared with random antenna selection+equal power allocation method (RAS+EPA), random antenna selection+power allocation method (RAS+PA), the antenna selection method based on genetic algorithm+equal power allocation method (GA-AS+EPA), and equal power allocation (EPA) these four methods. The EE of the proposed GA-AS+PA method is improved by 33.3% compared to the EPA method.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2022/7224731</identifier><language>eng</language><publisher>Oxford: Hindawi</publisher><subject>Antennas ; Communication ; Computational geometry ; Convexity ; Energy consumption ; Energy efficiency ; Genetic algorithms ; MIMO communication ; Optimization ; Power consumption ; Receivers & amplifiers</subject><ispartof>Wireless communications and mobile computing, 2022-04, Vol.2022, p.1-11</ispartof><rights>Copyright © 2022 Liping Du et al.</rights><rights>Copyright © 2022 Liping Du et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-7b95a3b9c658997f52b34e18dcff530350faeab867d5e5f5d7a6149afbfc53203</citedby><cites>FETCH-LOGICAL-c337t-7b95a3b9c658997f52b34e18dcff530350faeab867d5e5f5d7a6149afbfc53203</cites><orcidid>0000-0001-7804-962X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><contributor>Abbasi, Muhammad Inam</contributor><contributor>Muhammad Inam Abbasi</contributor><creatorcontrib>Du, Liping</creatorcontrib><creatorcontrib>Tan, Ying</creatorcontrib><creatorcontrib>Li, Yiming</creatorcontrib><creatorcontrib>Chen, Yueyun</creatorcontrib><title>On the Energy Efficiency of Multicell Massive MIMO with Antenna Selection and Power Allocation</title><title>Wireless communications and mobile computing</title><description>The energy consumption of massive multiple-input multiple-output (MIMO) systems increases with the number of antennas. Optimizing the energy efficiency (EE) of massive MIMO systems is one of the ways to achieve green communication. This paper proposes an EE optimization method that genetic algorithm-based antenna selection and power allocation (GA-AS+PA) for the downlink of a multicell massive MIMO system under the restriction of the users’ sum-rate. First, we use the genetic algorithm to determine the active transmitting antenna of each base station (BS). Then, the transmission power for each user is allocated using the convex optimization method. Finally, the EE of system is optimized under the achieved optimum BS’s transmit power and the number of active antennas. From the simulation results, the GA-AS+PA method can improve the EE of the system while meeting user sum-rate requirements, which achieves better performance compared with random antenna selection+equal power allocation method (RAS+EPA), random antenna selection+power allocation method (RAS+PA), the antenna selection method based on genetic algorithm+equal power allocation method (GA-AS+EPA), and equal power allocation (EPA) these four methods. 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| source | Wiley-Blackwell Open Access Titles; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Antennas Communication Computational geometry Convexity Energy consumption Energy efficiency Genetic algorithms MIMO communication Optimization Power consumption Receivers & amplifiers |
| title | On the Energy Efficiency of Multicell Massive MIMO with Antenna Selection and Power Allocation |
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