Investigation of various antenna design techniques and received signal strength prediction using machine learning for multiband 5G Application

dc.contributor.guideThulasi Bai, V
dc.coverage.spatialInvestigation of various antenna design techniques and received signal strength prediction using machine learning for multiband 5G Application
dc.creator.researcherBenisha, M
dc.date.accessioned2024-09-27T08:50:32Z
dc.date.available2024-09-27T08:50:32Z
dc.date.awarded2024
dc.date.completed2024
dc.date.registered
dc.description.abstractThe massive growth of mobile data evolution and numerous terminal devices requires a highly efficient mobile data network which affords superior communication. Shortage of available global bandwidth has led to allotment of 200 MHz band to service providers. These demands shift in spectrum up to several GHz for improvisation of quality of service (QoS) and reduced latency etc., using next generation mobile technology. newlineThe objective of this research work is to design suitable multiband antennas supporting frequency band from 2G (2nd Generation) to 5G (5th Generation) for meeting the demands of the current and upcoming 5G wireless technologies, which require incorporation of transceiver antennas for multiple wireless standards such as 2G, 3G, 4G, 5G, WiFi, Wimax, Bluetooth, etc., in a single computing device. This scenario has created a demand for wideband and multiband antennas for multi-standard wireless systems. Various countries have suggested and are working at 5G frequency ranging from 600 MHz to 71 GHz. The lower frequency spectrum is named as sub 6 GHz (Up to 6GHz) band while the higher frequency is known as mm wave spectrum (Above 6GHz). newline newline
dc.description.note
dc.format.accompanyingmaterialNone
dc.format.dimensions21cm.
dc.format.extentxxii,133p.
dc.identifier.urihttp://hdl.handle.net/10603/592103
dc.languageEnglish
dc.publisher.institutionFaculty of Information and Communication Engineering
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.relationp.124-132
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Information Systems
dc.subject.keywordEngineering and Technology
dc.subject.keywordGHz for improvisation
dc.subject.keywordglobal bandwidth
dc.subject.keywordmobile data evolution
dc.titleInvestigation of various antenna design techniques and received signal strength prediction using machine learning for multiband 5G Application
dc.title.alternative
dc.type.degreePh.D.

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