关于ofdma系统的跨层资源分配算法的研究

关于ofdma系统的跨层资源分配算法的研究

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时间:2019-02-25

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1、AbstractWiththedevelopmentofscienceandtechnology,thewidespreadpopularityofIntemettechnologyandtherapidriseofmobilecommunicationtechnology,theneedsforthepeoplearerapidlygrowing,whicharethevarietyofhigh.speeddatatransmissionservices.Alsothehigh-speeddatatransmissionserviceswillgraduallyrisetotheposit

2、ionwhichisthemostcompetitivebusinessinthewirelesscommunicationsystem.Thebiggestchallengesfromthecomingwirelesstransmissionsystemsaretoensureuser’Squalityofservice(QoS)andobtainthebetterspectrumutilization.Confrontingwithsuchchallenges,theresourceschedulingmechanismwillplayamoreandmoreimportantrolew

3、hichcouldflexiblydispatchthesystemresourcesandeffectivelyavoidthewasteofresources.Whilepeopleincreasinglyconcernedaboutthefuturewirelesstransmissionsystem,theresourceallocationproblemoftheOFDMAtechnologyhadobtainedmoreattentionsfromexpertsandscholars.TheyhaveproposedmanymethodsfortheOFDMAsystem,for

4、example,greedyalgorithm,theresourceallocationmethodsbasedOntheeffectivenessfunction(Rateutilityfunction,Delayutilityfunction),andtheresourceallocationmethodbasedongeneticalgorithm.Thesealgorithmsstudiedontheresourceallocationofthephysicallayerandthecross-layerbetweenphysicallayerandMAClayer,improve

5、dtheexistingclassicalalgorithmfromallaspects,andalsoachievedgoodresults.However,theseimprovedalgorithmsstillhadshortcomingswhichwerelowerspectralefficiency,difficulttoachieveandlargeamountofcomputation.So,theintroductionfromintelligentoptimizationalgorithmsbecomesahotresearch.GeneticAlgorithmisakin

6、dofnewoptimizationmethodwhichcombinedbiologicalevolutionarytheory,optimizationtechniquesandcomputertechnology.GeneticAlgorithmhasbeenmorewidelyappliedtomoreareas.Confrontingwithsomespecialproblemsandsomeproblemshadbeendifficulttosolvedwhentheyusedclassicalmethods,usinggeneticalgorithmhadobtainedbet

7、terresults.However,geneticalgorithmsstillhaveflawslikelocalconvergenceonsolvingthecross.1ayerresourceallocationofOFDMAsystem.Thispaperproposesacross—layerresourceallocationalgorithmbasedonadaptivegeneticalg

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