part1b.m

In this program, several statistical fading channel simulators using the Sum-of-Sinusoids (SoS)has b

clc;
close all;
echo off;

fd=100;
M=8;
N=4*M+2;
Ts=1e-4;
Ns=100;
maxlags=1000;
% u=[];

uc=zeros(M+1,10001);
us=zeros(M+1,10001);
u=zeros(M+1,10001);
norm_temp=zeros(M+1,maxlags);
corr_sum=zeros(M+1,maxlags);

   for i=1:10
       
   t=i:Ts:i+1;
    suma=0;
    sumb=0;
    for n=1:M+1
        if n<=M
            a=2*cos(pi*n/M);
            b=2*sin(pi*n/M);
            w=2*pi*fd*cos(2*pi*n/N);
        else
            a=sqrt(2)*cos(pi/4);
            b=sqrt(2)*sin(pi/4);
            w=2*pi*fd;
        end
        suma=suma+(a*cos(w*t));
        sumb=sumb+(b*sin(w*t));
            end
    uc(i,:)=sqrt(4/N)*suma;
    us(i,:)=sqrt(4/N)*sumb;
    u(i,:)=uc(i,:)+(j*us(i,:));
        
  %plot(i:Ts:i+1,abs(u));
 temp=xcorr(u(i,:),maxlags);
 norm_temp(i,:)=[temp(1:maxlags)]/norm(real(temp),inf);
 corr_sum(i,:)=flipdim(norm_temp(i,:),2);
 

 end
 plot([1:maxlags]*fd*Ts,corr_sum);
  title('Autocorrelation of U(t) at 10 different time instants');
 xlabel('Time lag s');
 ylabel('Autocorrelation');
 
  figure;
for i=1:10
  
[m,n]=hist(abs(u(i,:)),100);
su=sum(m);
m=m/su/(n(2)-n(1));
t1=i*ones(1,length(n));
plot3(t1,n,m);
grid on;
hold on;
end
title('PDF of U(t)');
ylabel('Bins');
zlabel('Normalised Probability ');
xlabel('Time');

% figure;
% for i=1:10
%     t=i:Ts:i+1;
%     
%     plot(t,u(i,:));
%     grid on;
%     hold on;
% end




% [m,n]=hist(u,50);
% su=sum(m);
% m=m/su;
% figure;
% % subplot(3,1,1),
% plot(n,m);
% title('PDF of Uc(t)');
% xlabel('Bins');
% ylabel('Normalised Probability');
% 
%