Usage of multi-objective genetic and multi-objective differential evolution algorithms on energy and spectral efficiencies in massive MIMO systems
| Main Authors: | Burak Kürşat Gül, Necmi Taşpınar |
|---|---|
| Format: | Article Journal |
| Bahasa: | eng |
| Terbitan: |
, 2020
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/4467654 |
| ctrlnum |
4467654 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Burak Kürşat Gül</creator><creator>Necmi Taşpınar</creator><date>2020-10-30</date><description>There is a significant increase in the use of wireless communication and it is expected that this increase will continue progressively. In the near future, cellular network technologies are expected to be capable of increasing the area throughput hundreds of times in order to cope with the increase in data traffic. Increasing spectral efficiency (SE) with massive multi-input multi-output (Massive MIMO) systems is one of the main methods used to meet these expectations. SE means the amount of information transmitted successfully with each complex sample. Increasing the transmission power and the number of active antennas while increasing the SE increases the amount of energy consumed to very high levels. The fact that high energy consumption is harmful to the environment and costly makes it important to increase energy efficiency (EE). Various studies are carried out with the aim of bringing optimum levels of the SE and EE parameters which has trade-off between each other. Multi-objective intelligent optimization techniques are applied on the trade-off for detecting optimum SE-EE values. In this paper, multi-objective genetic algorithm (MOGA) and multi-objective differential evolution algorithm (MODEA) are used to obtain optimum values of certain factors (amount of transmit power, number of active antennas and number of user equipments). At the last stage, the calculations made for all values of the mentioned factors and the optimization results (performed in a relatively short time compared to these calculations) are shown on the same graph.</description><identifier>https://zenodo.org/record/4467654</identifier><identifier>10.5281/zenodo.4467654</identifier><identifier>oai:zenodo.org:4467654</identifier><language>eng</language><relation>info:eu-repo/semantics/altIdentifier/doi/10.30574/gjeta.2020.5.1.0079</relation><relation>doi:10.5281/zenodo.4467653</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>Global Journal of Engineering and Technology Advances 5(1) 018-024.</source><subject>Massive MIMO; Spectral efficiency; Energy efficiency; Multi-objective genetic algorithm; Multi-objective differential evolution algorithm</subject><title>Usage of multi-objective genetic and multi-objective differential evolution algorithms on energy and spectral efficiencies in massive MIMO systems</title><type>Journal:Article</type><type>Journal:Article</type><recordID>4467654</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Journal:Journal |
| author |
Burak Kürşat Gül Necmi Taşpınar |
| title |
Usage of multi-objective genetic and multi-objective differential evolution algorithms on energy and spectral efficiencies in massive MIMO systems |
| publishDate |
2020 |
| topic |
Massive MIMO Spectral efficiency Energy efficiency Multi-objective genetic algorithm Multi-objective differential evolution algorithm |
| url |
https://zenodo.org/record/4467654 |
| contents |
There is a significant increase in the use of wireless communication and it is expected that this increase will continue progressively. In the near future, cellular network technologies are expected to be capable of increasing the area throughput hundreds of times in order to cope with the increase in data traffic. Increasing spectral efficiency (SE) with massive multi-input multi-output (Massive MIMO) systems is one of the main methods used to meet these expectations. SE means the amount of information transmitted successfully with each complex sample. Increasing the transmission power and the number of active antennas while increasing the SE increases the amount of energy consumed to very high levels. The fact that high energy consumption is harmful to the environment and costly makes it important to increase energy efficiency (EE). Various studies are carried out with the aim of bringing optimum levels of the SE and EE parameters which has trade-off between each other. Multi-objective intelligent optimization techniques are applied on the trade-off for detecting optimum SE-EE values. In this paper, multi-objective genetic algorithm (MOGA) and multi-objective differential evolution algorithm (MODEA) are used to obtain optimum values of certain factors (amount of transmit power, number of active antennas and number of user equipments). At the last stage, the calculations made for all values of the mentioned factors and the optimization results (performed in a relatively short time compared to these calculations) are shown on the same graph. |
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IOS16997.4467654 |
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ZAIN Publications |
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7213 |
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Cognizance Journal of Multidisciplinary Studies |
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Cognizance Journal of Multidisciplinary Studies |
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Multidisciplinary |
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Stockholm |
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INTERNASIONAL |
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2022-06-06T04:51:40Z |
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