An Efficient Neural Network-Based Prediction Scheme for Heterogeneous Networks

Int. J. Sociotechnology Knowl. Dev. Pub Date : 2020-04-01 DOI:10.4018/ijskd.2020040104

K. Hosny, Marwa M. Khashaba, Walid I. Khedr, F. Amer

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引用次数: 5

Abstract

Inmobilewirelessnetworks,thechallengeofprovidingfullmobilitywithoutaffectingthequalityof service(QoS)isbecomingessential.Thesechallengescanbeovercomeusinghandoverprediction. Theprocessofdeterminingthenextstationwhichmobileuserdesirestotransferitsdataconnection canbetermedashandoverprediction.Anewproposedpredictionschemeispresentedinthisarticle dependentonscanningallsignalqualitybetweenthemobileuserandallneighboringstationsinthe surroundingareas.Additionally,theproposedschemeefficiencyisenhancedessentiallyforminimizing theredundanthandover(unnecessaryhandovers)numbers.BothWLANandlongtermevolution (LTE)networksareusedintheproposedschemewhichisevaluatedusingvariousscenarioswith severalnumbersandlocationsofmobileusersandwithdifferentnumbersandlocationsofWLAN accesspointandLTEbasestation,allrandomly.Theproposedpredictionschemeachievesasuccess rateofupto99%inseveralscenariosconsistentwithLTE-WLANarchitecture.Tounderstandthe networkcharacteristicsforenhancingefficiencyandincreasingthehandoversuccessfulpercentage especiallywithmobilestationhighspeeds,aneuralnetworkmodelisused.Usingthetrainednetwork, itcanpredictthenexttargetstationforheterogeneousnetworkhandoverpoints.Theproposedneural network-basedschemeaddedasignificantimprovementintheaccuracyratiocomparedtotheexisting schemesusingonlythereceivedsignalstrength(RSS)asaparameterinpredictingthenextstation. Itachievesaremarkableimprovementinsuccessfulpercentageratioupto5%comparedwithusing onlyRSS.

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一种高效的基于神经网络的异构网络预测方案

Inmobilewirelessnetworks，thechallengeofprovidingfullmobilitywithoutaffectingthequalityof service_ (QoS)isbecomingessential.Thesechallengescanbeovercomeusinghandoverprediction。Theprocessofdeterminingthenextstationwhichmobileuserdesirestotransferitsdataconnection canbetermedashandoverprediction。Anewproposedpredictionschemeispresentedinthisarticle dependentonscanningallsignalqualitybetweenthemobileuserandallneighboringstationsinthe surroundingareas。Additionally、theproposedschemeefficiencyisenhancedessentiallyforminimizing theredundanthandover(unnecessaryhandovers)numbers。BothWLANandlongtermevolution (LTE)networksareusedintheproposedschemewhichisevaluatedusingvariousscenarioswith severalnumbersandlocationsofmobileusersandwithdifferentnumbersandlocationsofWLAN accesspointandLTEbasestation、allrandomly。Theproposedpredictionschemeachievesasuccess rateofupto99%inseveralscenariosconsistentwithLTE-WLANarchitecture。Tounderstandthe networkcharacteristicsforenhancingefficiencyandincreasingthehandoversuccessfulpercentage especiallywithmobilestationhighspeeds，aneuralnetworkmodelisused。Usingthetrainednetwork, itcanpredictthenexttargetstationforheterogeneousnetworkhandoverpoints。Theproposedneural network-basedschemeaddedasignificantimprovementintheaccuracyratiocomparedtotheexisting schemesusingonlythereceivedsignalstrength(RSS)asaparameterinpredictingthenextstation。Itachievesaremarkableimprovementinsuccessfulpercentageratioupto5%comparedwithusing onlyRSS。

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Int. J. Sociotechnology Knowl. Dev.

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