无线2Word文档格式.docx

无线2Word文档格式.docx

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STEPSANDRESULTSOFTHEEXPERIMENTS

1.Generateanmsequence:

2.Calculatetheautocorrelationsequenceofit:

3.Thenwedefineainputsignalandmultiplythemsequencewithit:

4.Afterthemultiplicationofmsequenceandmodulationwithoutnoise:

5.WeaddtheAddedGaussianWhiteNoiseandgetthesignalasbelow:

6.Thenwedothedemodulationpart.

7.AfterfiltertheAGWNanddoingsamplingjudgment,wegettheinputsignalagain:

CONCLUSION

Fromthislab,wehavelearnedtheMATLABlanguageofrealizingdirectsequencespreadspectrum,whichiswidelyusedinmoderncommunicationandmilitarycommunication.Besides,wehavegotfamiliarwithmuchrelativemathematicalknowledge,suchascorrelationandorthogonality,bythislab.Inthisexperience,wemodeledadirectsequencespreadprocedure,andthatgiveusadeeperunderstandingtothislecture.

APPENDIX

main:

connections='

1010001110100001'

;

%n阶反馈寄存器系数

c=bin2dec（connections）;

[fa]=smodulate（c,8,1,64,128）;

figure（）;

plot（f）;

axis（[0256-22]）;

xlabel（'

ModulatedSignal'

）;

plot（a）;

axis（[0128-22]）;

OriginalSignal'

g=noise（f,8）;

plot（g）;

ModulatedSignalwithNoise'

x=sdemodulate（f,c,8,1,64,128）;

x=x*2-1;

comp（a,x）;

plot（x）;

DemodulatedSignal'

chip:

function[out,Tc]=chip（ms,T）

Tc=1/64;

N_s=8;

N=length（ms）;

dt=Tc/N_s;

t=0:

dt:

N*Tc-dt;

gt=ones（1,N_s）;

mt=sigexpand（ms,N_s）;

mt=conv（mt,gt）;

out=mt（1:

N*N_s）;

comp:

function[out]=comp（in1,in2）;

l1=length（in1）;

l2=length（in2）;

ifl1~=l2

out=-1;

else

d=0;

l=l1;

fori=1:

l

c=in1（l）-in2（l）;

d=d+c;

end

out=d;

ms_gen:

function[out,l]=ms_gen2（g,state,N）

lA=length（g）;

iflA==1

connections=dec2bin（g）-'

0'

else

connections=g;

lB=length（connections）;

regState=dec2bin（state,lB-1）-'

l=2^（lB-1）-1;

loop=min（l,N）;

fork=1:

loop

period（k）=regState（lB-1）;

an=rem（sum（connections（2:

end）.*regState）,2）;

regState=[anregState（1:

lB-2）];

ifN>

l

i=fix（N/l）;

j=rem（N,l）;

fori1=1:

i

out（（（i1-1）*l+1）:

（i1*l））=period（1:

l）;

end

out（（i*l+1）:

N）=period（1:

j）;

out=period;

d=1-2*out;

l=min（l,N）;

auto_corr=conv（d,d（l:

-1:

1））/l;

stem（auto_corr（l:

end））;

axis（[0100-22]）;

Autocorrelationofmsequence'

msignal:

function[out,d]=msignal（ms,Tc）

A=1;

Sr=1;

N1=length（ms）;

N=N1*Tc/N_s;

Ts=1/Tc;

fc=1;

dt=1/fc/N_s;

N*Ts-dt;

Lt=length（t）;

d=sign（randn（1,N））;

dd=sigexpand（d,fc*N_s*Ts）;

gt=ones（1,fc*N_s*Ts）;

d_NRZ=conv（dd,gt）;

out=d_NRZ（1:

N1）.*ms;

AGWN:

function[out]=noise（in,EsN0dB）

l=length（in）;

sigma=sqrt（10.^（-EsN0dB/10）/2）;

out=in+sigma*randn（1,l）;

sdemodulate:

function[out]=sdemodulate（in,g,state,symRate,spreG,cf）

le=length（in）;

fc=cf;

N=le/cf/N_s;

Ts=symRate;

Tc=Ts/spreG;

1*Ts-dt;

Lt=length（t）

N1=N/Tc;

ms=ms_gen2（g,state,N1）;

msd=1-ms*2;

msd=sigexpand（msd,fc*Tc*N_s）;

gc=ones（1,fc*Tc*N_s）;

md=conv（msd,gc）;

md=md（1:

le）;

ht=A*cos（2*pi*fc*t）;

in2=in.*md;

psk=reshape（in2'

（1/dt/Ts）,le*dt/Ts）;

psk=psk'

le*dt/Ts

s=sum（psk（i,:

）.*ht）;

ifs>

0

out（i）=1;

else

out（i）=0;

sigexpand:

function[out]=sigexpand（d,M）

N=length（d）;

out=zeros（M,N）;

out（1,:

）=d;

out=reshape（out,1,M*N）;

smodulate:

function[out,d]=smodulate（g,state,symRate,spreG,cf）

N=1000;

Lt）;

d_NRZ=conv（dd,gt）;

d_2psk=d_NRZ（1:

Lt）.*md;

out=d_2psk.*ht;