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dc.contributor.authorIbrahim Salah
dc.contributor.authorKamel Hussein Rahouma
dc.contributor.authorHussein, Aziza
dc.contributor.authorMohamed M Mabrook
dc.date.accessioned2023-06-04T10:09:33Z
dc.date.available2023-06-04T10:09:33Z
dc.date.issued2023-03-16
dc.identifier.doi10.24425/ijet.2023.144349en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/887
dc.description.abstractOn fifth-generation wireless networks, a potential massive MIMO system is used to meet the ever-increasing request for high-traffic data rates, high-resolution streaming media, and cognitive communication. In order to boost the trade-off between energy efficiency (EE), spectral efficiency (SE), and throughput in wireless 5G networks, massive MIMO systems are essential. This paper proposes a strategy for EE 5G optimization utilizing massive MIMO technology. The massive MIMO system architecture would enhance the trade-off between throughput and EE at the optimum number of working antennas. Moreover, the EE-SE tradeoff is adjusted for downlink and uplink massive MIMO systems employing linear precoding techniques such as Multiple - Minimum Mean Square Error (M-MMSE), Regularized Zero Forcing (RZF), Zero Forcing (ZF), and Maximum Ratio (MR). Throughput is increased by adding more antennas at the optimum EE, according to the analysis of simulation findings. Next, utilizing M MMSE instead of RZF and ZF, the suggested trading strategy is enhanced and optimized. The results indicate that M-MMSE provides the best tradeoff between EE and throughput at the determined optimal ratio between active antennas and ctive users equipment’s (UE)en_US
dc.language.isoenen_US
dc.publisherPolish Academy of Sciences Committee of Electronics and Telecommunicationsen_US
dc.subjectMassive MIMOen_US
dc.subjectSpectral Efficiencyen_US
dc.subjectEnergy Efficiencyen_US
dc.titleThroughput, Spectral, and Energy Efficiency of 5G Massive MIMO Applications Using Different Linear Precoding Schemesen_US
dc.source.journalInternational Journal of Electronics and Telecommunicationsen_US
dc.source.volumevol. 69en_US
dc.source.issueNo 1en_US
refterms.dateFOA2023-06-04T10:09:33Z
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labNAen_US
dc.subject.KSAICTen_US
dc.contributor.ugstudentNAen_US
dc.contributor.alumnaeNAen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.pgstudentNAen_US
dc.contributor.firstauthorIbrahim Salah


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