generate binary data b_k using randint.
2. define the sampling time, and the data rate. For example, data rate can be R=10kbps, T=1/R, and sampling time Ts=T/40;
3. according to the above define your pulse shape (wave form of the different line codes).
4. obtain the x(t)=\sum_{k=-100}^{100} d_k p(t-kT)
d_k should be correctly mapped from the b_ks generated above.
5. plot the resulting x(t) for the different line codes.
6. obtain the autocorrelation function of x(t) (you may need to average over many realization (10-50 realizations should be enough to get smooth curve).
7. obtain the power spectral density by performing FFT of the autocorrelation function.
8. plot the power spectral density.


solution :
function f1=unipolar_nrz(b,R,Ns)

b=randint(1,100);
R=10000; %data rate: 10kbps
Tb=1/R; %bit duration
Nb=length(b);
Ts=Tb/4000;
Fs=1/Ts;
Ns=100; %40 samples/bit
d=b;
pulse=ones(1,Ns);
t=0:Ts(Ns*Nb)*Ts)-Ts;

x=kron(d,pulse); %Kronecker tensor product
%y1 = wgn(100,1,0);
plot(t,x)
%y= X+ 0.00001*y1;
D= pwelch(x,33,32,[],Fs,'twosided');
hold
%D2=D+randn;
plot(fftshift(D))

end




>>b = rand(100,1);
>>R=10000;
>>Tb=1/R;
>>Ts=Tb/40;
>>Nb=length(b);
>>Ns=40;
>>pulse =[ones(1,Ns/2), -ones(1,Ns/2)];
>>t=0:Ts(Ns*Nb).*Ts)-Ts;
>>f=0:0.05*Rb:2*Rb;
>>x=f*Tb;
>>d=b;
>>x=kron(d,pulse);


n=1
m=1
b = randint (n)
b = randint (n,m)
b = randint (n,m,[0:1])
R=10000
T=1/R
%sampling time
Ts=T/40


if any can complete and correct , i am ganna be so plzd