基于密度演化與有限碼長(zhǎng)分析的低密度校驗(yàn)碼構(gòu)造

基于密度演化與有限碼長(zhǎng)分析的低密度校驗(yàn)碼構(gòu)造

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1、CONSTRUCTIONOFLOW-DENSITYPARITY-CHECKCODESBASEDONDENSITYEVOLUTIONANDFINITE-LENGTHANALYSISABSTRACTAsakindofchannelcodingtechniquewithwideapplication,LDPCcodeshavebeenadoptedinDVB-S2and802.16e.Sincetheperformancecanachieveorexceedturbocodeswhileencodinganddecodingcomplex

2、ityisquitelowduetothesparsenessofparitymatrix,LDPCcodeshavebecomethefocusofcodingtheory.IncomparisonwithregularLDPCcodes,irregularcodescanachievebetterperformanceintermsofnoisethreshold,whichislargelyaffectedbydegreedistributionpair.Usingdensityevolution,theperformance

3、ofcodeswithsamedegreedistributioncanbedetermined.Thus,degreedistributionpaircanbeoptimizedonthebasisofdensityevolution,whichmakesitpossibletofindgoodcodes.However,optimaldegreedistributionsalwaysresultinpoorerrorfloor,soitisnecessarytoaddconstraintondegreedistributionp

4、airsandtakefinite-lengtheffectsintoconsideration.Ontheotherhand,forparticulardegreedistributionpair,thoughcodesunderrandomconstructionmayhavesimilarperformance,theparitymatrixshouldhavewell-designedstructureforthesakeofimplementation.Withstudyofaboveissues,LDPCcodeswit

5、hgoodperformanceandlowimplementationcomplexitycanbeconstructedandappliedincommunicationsystemsforerror-correcting.Inthispaper,firstlydecodingalgorithmsincludingbeliefpropagation,min-sumalgorithmisanalyzedunderdensityevolution.Thendensityevolutionoflayereddecodingisdedu

6、ced,andperformancecomparisonofvariousdecodingalgorithmsismadeintermsofthresholdandconvergence.Thusoptimalscalefactorformodifiedmin-sumalgorithmisobtainedanditisshownthatwithlayereddecoding,iterationtimescanbereducedbyalmosthalf.Inaddition,withfinite-lengthanalysis,thei

7、mpactonerrorfloorbystructureofparitymatrixisstudied.Sinceerrorfloorismainlycausedbycycleswithlowextrinsicmessagedegree,someconstraintsonvariabledegreedistributionareputforward,whichcanbeusedtooptimizedegreedistributionpair.Finally,withconsiderationofencodinganddecoding

8、complexity,aconstructionmethodofirregularLDPCcodesispresented.Withconstraintonnumberofdegree-2variablenodesandapproxi

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