通信仿真双语教学ppt

Chapter5BasebandDigitalTransmissionMainContents:Inthischapter,weconsiderseveralbasebanddigitalmodulationanddemodulationtechniquesfortransmitting.Inabinarycommunicationsystem,binarydataconsistingofasequenceof0’sand1’saretransmittedbymeansoftwosignalwaveforms,say,s0(t)ands1(t).SupposethatthedatarateisspecifiedasRbitspersecond.TheneachbitismappedintoacorrespondingsignalwaveformaccordingtotherulebbTttsTtts010010Whereisdefinedasthebittimeinterval.Weassumethatthedatabits0and1areequallyprobable-thatis,eachoccurswithprobability½andaremutuallystatisticallyindependent.RTb1Thechannelthroughwhichthesignalistransmittedisassumedtocorruptthesignalbytheadditionofnoise,denotedasn(t),whichisasamplefunctionofawhiteGaussianprocesswithpowerspectrumwatts/hertz.SuchachanneliscalledanadditivewhiteGaussiannoise（AWGN）channel.Consequently,thereceivedsignalwaveformisexpressedasbiTtitntstr01,0Thetaskofthereceiveristodeterminewhethera0ora1wastransmittedafterobservingthereceivedsignalr(t)intheinterval.Thereceiverisdesignedtominimizetheprobabilityoferror.Suchareceiveriscalledtheoptimumreceiver.bTt05.2.1OptimumReceiverfortheAWGNChannelawgn()AWGNAddwhiteGaussiannoisetoasignal.1、Y=AWGN(X,SNR)addswhiteGaussiannoisetoX.TheSNRisindB.ThepowerofXisassumedtobe0dBW.IfXiscomplex,thenAWGNaddscomplexnoise.x(1:10^6)=2;y=awgn(x,10);mean(y)var(y)2、Y=AWGN(X,SNR,SIGPOWER)whenSIGPOWERisnumeric,itrepresentsthesignalpowerindBW.（WhenSIGPOWERis'measured',AWGNmeasuresthesignalpowerbeforeaddingnoise.）3、Y=AWGN(X,SNR,SIGPOWER,STATE)resetsthestateofRANDNtoSTATE.4、Y=AWGN(...,POWERTYPE)specifiestheunitsofSNRandSIGPOWER.POWERTYPEcanbe'db'or'linear'.IfPOWERTYPEis'db',thenSNRismeasuredindBandSIGPOWERismeasuredindBW.IfPOWERTYPEis'linear',thenSNRismeasuredasaratioandSIGPOWERismeasuredinWatts.5.2.2AWGNChannelBlockAddwhiteGaussiannoisetotheinputsignal.TheAWGNChannelblockaddswhiteGaussiannoisetoarealorcomplexinputsignal.Whentheinputsignalisreal,thisblockaddsrealGaussiannoiseandproducesarealoutputsignal.Whentheinputsignaliscomplex,thisblockaddscomplexGaussiannoiseandproducesacomplexoutputsignal.Thisblockinheritsitssampletimefromtheinputsignal.Initialseed:TheseedfortheGaussiannoisegenerator.Mode:Themodebywhichyouspecifythenoisevariance:Signaltonoiseratio(Es/No),Signaltonoiseratio(SNR),Variancefrommask,orVariancefromport.Es/No(dB):Theratioofsignalenergypersymboltonoisepowerspectraldensity,indecibels.ThisfieldappearsonlyifModeissettoEs/No.SNR(dB):Theratioofsignalpowertonoisepower,indecibels.ThisfieldappearsonlyifModeissettoSNR.Inputsignalpower(watts):Therootmeansquarepoweroftheinputsymbols(ifModeisEs/No)orinputsamples(ifModeisSNR),inwatts.ThisfieldappearsonlyifModeissettoeitherEs/NoorSNR.Symbolperiod(s):Thedurationofachannelsymbol,inseconds.ThisfieldappearsonlyifModeissettoEs/No.Variance:ThevarianceofthewhiteGaussiannoise.ThisfieldappearsonlyifModeissettoVariancefrommask.5.3EyediagramEYEDIAGRAMEYEDIAGRAM(X,N)generatesaneyediagramofXwithNsamplespertrace.Nmustbeanintegergreaterthan1.EYEDIAGRAM(X,N,PERIOD)generatesaneyediagramofXwithspecifiedtraceperiod.EYEDIAGRAM(X,N,PERIOD,OFFSET)generatesaneyediagramofXwithanoffset.M=16;Fd=1;Fs=10;Pd=100;msg_d=randint(Pd,1,M);msg_a=modmap(msg_d,Fd,Fs,'qsk',M);delay=3;rcv=rcosflt(msg_a,Fd,Fs,'fir/normal',.5,delay);propdelay=delay.*Fs/Fd+1;rcv1=rcv(propdelay:end-(propdelay-1),:);N=Fs/Fd;offset1=0;h1=eyediagram(rcv1,N,1/Fd,offset1);set(h1,'Name','EyeDiagramDisplayedwithNoOffset');offset2=2;h2=eyediagram(rcv1,N,1/Fd,offset2,'r-');set(h2,'Name','EyeDiagramDisplayedwithOffsetofTwo');DiscreteEyeDiagramScope