小波域的数字调制信号识别及码速率估计的中期报告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,weaimtodevelopasystem