Joint Linear Receiver Design and Power Allocation Using Alternating Optimization Algorithms.pdf

Joint Linear Receiver Design and Power Allocation Using Alternating Optimization Algorithms.pdf

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时间:2019-02-28

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1、IEEETRANSACTIONSONVEHICULARTECHNOLOGY,VOL.61,NO.9,NOVEMBER20124129JointLinearReceiverDesignandPowerAllocationUsingAlternatingOptimizationAlgorithmsforWirelessSensorNetworksTongWang,RodrigoC.deLamare,SeniorMember,IEEE,andAnkeSchmeink,Member,IEEEAbstractInthispaper,weconsideratwo-hopwirelesss

2、ensordiversitywithcooperationbetweenthenodes[2],[4],[5].Inanetwork(WSN)withmultiplerelaynodeswheretheamplify-and-cooperativeWSN,nodesrelaysignalstoeachothertoprop-forward(AF)schemeisemployed.Wepresentstrategiestodesignagateredundantcopiesofthesamesignalstothedestinationjointlylinearreceiver

3、sandthepower-allocationparametersvianodes.Amongtheexistingrelayingschemes,theamplify-and-analternatingoptimizationapproachsubjecttoglobal,individual,andneighbor-basedpowerconstraints.Twodesigncriteriaareforward(AF)anddecode-and-forward(DF)schemesaretheconsidered:Thefirstcriterionminimizesthe

4、mean-squareerrormostpopularapproaches[6].IntheAFscheme,therelay(MSE),andthesecondcriterionmaximizesthesum-rate(SR)ofnodesamplifythereceivedsignalandrebroadcasttheamplifiedtheWSN.Wederiveconstrainedminimummean-squareerrorsignalstowardthedestinationnodes.IntheDFscheme,the(MMSE)andconstrainedma

5、ximumsum-rate(MSR)expressionsrelaynodesfirstdecodethereceivedsignalsandthenregenerateforthelinearreceiversandthepower-allocationparametersthatcontaintheoptimalcomplexamplificationcoefficientsforeachnewsignalstothedestinationnodessubsequently.relaynode.ComputersimulationsshowgoodperformanceofDu

6、etothelimitationsinsensornodepower,computationalourproposedmethodsintermsofbiterrorrate(BER)orSRcapacity,andmemory[1],somepower-allocationmethodshavecomparedwiththemethodwithequalpowerallocationandtoabeenproposedforWSNstoobtainthebestpossibleSNRtwo-stagepower-allocationtechnique.Furthermore

7、,themethodsorthebestpossiblequalityofservice(QoS)[7],[8]atthewithneighbor-basedconstraintsbringflexibilitytobalancetheperformanceagainstthecomputationalcomplexityandtheneeddestinations.Themajorityofthepreviousliteratureconsidersforfeedbackinformation,whic

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