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dc.contributor.authorHussein, Aziza
dc.contributor.authorsalah, ibrahim
dc.date.accessioned2022-11-09T11:32:42Z
dc.date.available2022-11-09T11:32:42Z
dc.date.issued2022
dc.identifier.doi10.1007/s11082-021-03507-5en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/201
dc.description.abstractGiven that, the exponential pace of growth in wireless traffic has continued for more than a century, wireless communication is one of the most influential innovations in recent years. Massive Multiple-Input Multiple-Output (M-MIMO) is a promising technology for meeting the world's exponential growth in mobile data traffic, particularly in 5G networks. The most critical metrics in the massive MIMO scheme are Spectral Efficiency (SE) and Energy Efficiency (EE). For single-cell M-MIMO uplink transmission, energy and spectral-efficiency trade-offs have to be estimated by optimizing the number of base station antennas versus the number of active users. This paper proposes an adaptive optimization technique focusing on maximizing Energy Efficiency at full spectral efficiency using a Genetic Algorithm (GA) optimizer. The number of active antennas is determined according to the change in the number of active users based on the proposed GA scheme that optimizes the EE in the M-MIMO system. Simulation results show that the GA optimization technique achieved the maximum energy efficiency of the 5G M-MIMO platform and the maximum efficiency in the trade-off process.en_US
dc.publisherSpringer Nature
dc.subjectAdaptive antennaen_US
dc.subject5G networksen_US
dc.subjectMassive-MIMOen_US
dc.subjectSpectral efficiencyen_US
dc.subjectEnergy efficiency optimizationen_US
dc.subjectGenetic Algorithmen_US
dc.titleEnergy efficiency optimization in adaptive massive MIMO networks for 5G applications using genetic algorithmen_US
dc.typeArticleen_US
dc.source.journalOptical and Quantum Electronicsen_US
refterms.dateFOA2022-11-09T11:32:42Z
dc.contributor.researcherElectrical and Computer Engineeringen_US


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