小波域的數(shù)字調(diào)制信號識別及碼速率估計的中期報告LITERATUREREVIEWInrecentyears,theuseofwavelettransformfordigitalsignalprocessinghasbecomeincreasinglypopularduetoitsabilitytoanalyzesignalsinbothtimeandfrequencydomainsimultaneouslyanditsabilitytocapturebothshort-termandlong-termbehaviorofsignals.Inparticular,wavelettransformhasbeenusedformodulationrecognitionandsignalclassificationinvariouscommunicationsystems.Modulationrecognitionisanimportanttaskinmanysignalprocessingapplications,includingradar,wirelesscommunication,andsatellitecommunication.Itinvolvesidentifyingthemodulationparametersofareceivedsignal,suchasmodulationscheme,carrierfrequency,andsymbolrate.Therearevariousmethodsformodulationrecognition,suchasstatisticalclassifiers,artificialneuralnetworks,andsupportvectormachines.However,thesemethodsrequireconsiderablecomputationalresourcesandmaysufferfromoverfittingandlimitedaccuracy.Recently,wavelettransformhasbeenusedformodulationrecognitionduetoitsabilitytocapturethefrequencyandtime-varyingnatureofsignals.Inparticular,waveletpackettransform(WPT)hasbeenusedtoextractrelevantfeaturesfromthesignalformodulationrecognition.Theextractedfeaturesarethenusedasinputstomachinelearningalgorithmsforclassification.Anotherimportanttaskindigitalsignalprocessingistheestimationofthesymbolrateofareceivedsignal.Symbolrateestimationiscriticalinvariouscommunicationsystems,asitenablessynchronizationbetweenthetransmitterandreceiver.Therearevarioustechniquesforsymbolrateestimation,includingautocorrelationandmaximumlikelihoodestimators.However,thesetechniquesmaysufferfrompoorperformanceinnoisyandmultipathchannels.Wavelettransformhasbeenusedforsymbolrateestimationduetoitsabilitytocapturethetime-varyingnatureofsignals.Inparticular,continuouswavelettransform(CWT)andWPThavebeenusedforsymbolrateestimationinvariouscommunicationsystems.Theextractedfeaturesfromthewavelettransformareusedtoestimatethesymbolrateusingmaximumlikelihoodorautocorrelation-basedtechniques.PROBLEMSTATEMENTInthisproject,weaimtodevelopasystemformodulationrecognitionandsymbolrateestimationofdigitalsignalsinthewaveletdomain.Thesystemwillconsistofthefollowingcomponents:1.Wavelettransform:Thereceivedsignalwillbeanalyzedusingwavelettransformtoextracttherelevantfeatures.2.Featureextraction:TherelevantfeatureswillbeextractedfromthewaveletcoefficientsusingWPT.3.Modulationrecognition:Theextractedfeatureswillbeusedformodulationrecognitionusingmachinelearningalgorithmssuchassupportvectormachines,decisiontrees,andk-nearestneighbors.4.Symbolrateestimation:Theextractedfeatureswillbeusedforsymbolrateestimationusingmaximumlikelihoodorautocorrelation-basedtechniques.Thesystemwillbetestedonvariousdigitalsignalswithdifferentmodulationschemesandsymbolratesinordertoevaluateitsperformance.METHODOLOGYTheoverallmethodologyforthisprojectcanbedividedintothefollowingsteps:1.Datacollection:Digitalsignalswithdifferentmodulationschemesandsymbolrateswillbecollectedusingasoftware-definedradioreceiver.2.Wavelettransform:Thereceivedsignalswillbeanalyzedusingwavelettransformtoextracttherelevantfeatures.3.Featureextraction:TherelevantfeatureswillbeextractedfromthewaveletcoefficientsusingWPT.4.Modulationrecognition:Theextractedfeatureswillbeusedformodulationrecognitionusingmachinelearningalgorithmssuchassupportvectormachines,decisiontrees,andk-nearestneighbors.5.Symbolrateestimation:Theextractedfeatureswillbeusedforsymbolrateestimationusingmaximumlikelihoodorautocorrelation-basedtechniques.6.Evaluation:Theperformanceofthesystemwillbeevaluatedbycalculatingtheaccuracyofmodulationrecognitionandsymbolrateestimationonthetestsignals.EXPECTEDOUTCOMESTheexpectedoutcomesofthisprojectareasfollows:1.Developmentofasystemformodulationrecognitionandsymbolrateestimationofdigitalsignalsinthewaveletdomain.2.Evaluationoftheperformanceoftheproposedsystemonvariousdigitalsignalswithdifferentmodulationschemesandsymbolrates.3.Identificationofthemosteffective