%ray1.m 
% Rayleigh fading simulator
% Uses the method of filtering complex Gaussians with approximate doppler spread.
%
% These variables may be defined before the program runs:
%   ftype   type of shaping filter used (default =0 gives predefined bathtub curve)
%           otherwise ftype = >0 to 0.5 is the normalised LPF cutoff
%
% Richard Wyrwas, 2 February 1995,  with additions by Bill Cowley 4/97 
 function [iout,qout]=rayleigh(idata,qdata)
 
 a=length(idata);
 b=length(qdata);

%disp('ray1: Rayleigh signal simulation by filtering random I and Q signals.');
%disp('(press space to continue)');

if exist('ftype')~=1,    ftype =0;  end;	% check if filter type is defined
if ftype==0 
    %disp('Using predefined bathtub shape')
    %  define a frequency vector and a magnitude vector to simulate the classic 'bathtub' shape
    f=(0.0:0.05:1.0);
    m=[1.0,1.2,1.5,1.9,2.8,4.0,6.0,9.0,15.0,25.0,1.00,0.05,0.03,0.02,0.01,0.005,0.005,0.005,0.005,0.005,0.005];
   
    B=fir2(16,f,m);		% design an FIR filter based on the shape above. 
else 
    B = fir1(60, ftype*2);	% design LPF using ftype as Fcutoff/Fs 
    
end

x=randn(1,a);	% make 2048 normally distributed random number for I and Q
y=randn(1,b);
% z=sqrt(x.*x + y.*y);
x1=filter(B,1,x);	% filter the I and Q signals 
y1=filter(B,1,y);
R=x1+j.*y1;		% generate the complex baseband Rayleigh signal 
Mag=abs(R);	

iout=idata.*Mag(1:a);
qout=qdata.*Mag(1:b);