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一种基于最大似然的SOQPSK联合估计优化算法.docx

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一种基于最大似然的SOQPSK联合估计优化算法 Title:AMaximumLikelihood-BasedJointEstimationOptimizationAlgorithmforSOQPSK Abstract: Theaimofthispaperistoproposeamaximumlikelihood-basedjointestimationoptimizationalgorithmforSerial-OrthogonalQuadraturePhaseShiftKeying(SOQPSK).SOQPSKisadigitalmodulationschemewidelyusedinwirelesscommunicationsystemsduetoitshighspectralefficiencyandrobustnessagainstfadingchannels.TheproposedalgorithmimprovestheaccuracyofchannelestimationandsymbolrecoveryinSOQPSKsignals,enablingbettersystemperformance. 1.Introduction SOQPSKisapopularmodulationschemethatisoftenusedincommunicationsystemsoperatinginbandwidth-constrainedscenarios.Itexhibitssuperiorperformanceintermsofbiterrorrate(BER)andisrobustagainstmultipathfadingandinterference.However,accurateestimationofthechannelparametersandsymbolrecoveryisessentialforachievingoptimalsystemperformance. 2.ChannelEstimationinSOQPSK Thefirststepinthejointestimationoptimizationalgorithmistheestimationofthechannelparameters.Themaximumlikelihood(ML)criterionisusedtoestimatethechanneltaps.Byformulatingtheproblemasalikelihoodmaximizationproblem,thealgorithmiterativelyadjuststhechannelparameterstomaximizethelikelihoodfunction. 3.JointSymbolRecoveryinSOQPSK Afterchannelestimation,thejointsymbolrecoveryalgorithmaimstojointlyestimatethetransmittedsymbols.Thisisanoptimizationproblembecausethereceivedsymbolsareaffectedbychannelimpairments,noise,andinterference.TheproposedalgorithmusestheMLcriteriontoiterativelyestimatethephaseandamplitudeofthetransmittedsymbolsbasedonthereceivedsymbols. 4.PerformanceEvaluation Simulationsandperformanceevaluationareconductedtoassesstheperformanceoftheproposedalgorithm.Severalmetricssuchasbiterrorrate(BER),signal-to-noiseratio(SNR),andspectralefficiencyareconsideredtoevaluatetheperformance.Theresultsarecomparedwithexistingalgorithms,demonstratingthesuperiorityoftheproposedalgorithmintermsofaccuracyandsystemperformance. 5.Discussion ThealgorithmpresentedinthispaperoffersseveraladvantagesoverexistingSOQPSKe