rayleigh.m

无线移动信道的matlab源码 用于对无线通信信道的编程实现

function row = rayleigh( fd, fs, Ns )
% r = rayleigh(fd,fs,N)
%
% A Rayleigh fading simulator based on Clarke's Model
% Creates a Rayleigh random process with PSD determined
% by the vehicle's speed.
%
%INPUTS:
%   fd = doppler frequency
%        set fd = v*cos(theta)/lambda
%        v = velocity (meters per second)
%        lambda = carrier wavelength (meters)
%        theta = angle w/ respect to tangent (radians).
%   fs = sample frequency (Samples per second)
%   Ns = number of samples of the Rayleigh fading
%        process to produce
%
% OUTPUTS:
%   r  = row vector containing Ns samples of the Rayleigh
%        fading process 
%
% Author:  Matthew C. Valenti
%          Mobile and Portable Radio Research Group
%          Virginia Tech    
%
% For Academic Use Only
%v = input('enter the velocity (meters per second): ')
%lambda = input('enter the carrier wavelength (meters): ')
%theta = input('enter the angle w/ respect to tangent (radians): ')
%fd = v*cos(theta)/lambda

%fd = input('doppler frequency: ');
%fs = input('enter the sample frequency (Samples per second): ');
%Ns = input(' enter the number of samples of the Rayleigh fading process to produce: ');
N = 8;
while(N)
   if (N < 2*fd*Ns/fs) 
      N = 2*N;
   else
      break;
   end
end 

% number of ifft points (for smoothing)
N_inv = ceil(N*fs/(2*fd));

% determine the frequency spacings of signal after FFT
delta_f = 2*fd/N;

% determine time spacing of output samples
delta_T_inv = 1/fs;

%%%%%%%%%%% Begin Random Input Generation %%%%%%%%%%%%
% fprintf( 'Generating Input\n');
% generate a pair of TIME DOMAIN gaussian i.i.d. r.v.'s
I_input_time1 = randn(1,N);
Q_input_time1 = randn(1,N);

% take FFT
I_input_freq = fft(I_input_time1);
Q_input_freq = fft(Q_input_time1);

%%%  Generate Doppler Filter's Frequency Response %%%
% fprintf( 'Generating Doppler Filter Function\n');
% filter's DC component
SEZ(1) = 1.5/(pi*fd);

% 0 < f < fd 
for j=2:N/2
   f(j) = (j-1)*delta_f;
   SEZ(j) = 1.5/(pi*fd*sqrt(1-(f(j)/fd)^2));
   SEZ(N-j+2) = SEZ(j);
end

% use a polynomial fit to get the component at f = fd
% p = polyfit( f(N/2-3:N/2), SEZ(N/2-3:N/2), 3);
% SEZ(N/2+1) = polyval( p, f(N/2)+delta_f );
%k = N/2 - 1;

%For deep fading, picking tangential point in filter
k = 3;

%p = polyfit( f(N/2-k:N/2), SEZ(N/2-k:N/2), k );

%SEZ(N/2+1) = polyval( p, f(N/2)+delta_f );
SEZ(N/2+1) = 1.8;
%%%%%%%% Perform Filtering Operation %%%%%%%%%%%%%%
% fprintf( 'Computing Output\n' );
% pass the input freq. components through the filter
I_output_freq = I_input_freq .* sqrt(SEZ);
Q_output_freq = Q_input_freq .* sqrt(SEZ);

% take inverse FFT
I_temp = [I_output_freq(1:N/2) zeros(1,N_inv-N) I_output_freq(N/2+1:N)];
I_output_time = ifft(I_temp);

Q_temp = [Q_output_freq(1:N/2) zeros(1,N_inv-N) Q_output_freq(N/2+1:N)];
Q_output_time = ifft(Q_temp);

% make vector of times (in milliseconds)
% for j = 1:N_inv
%   t(j) = (j-1)*delta_T_inv*1000;
% end

% take magnitude squared of each component and add together
for j=1:N_inv
%    r(j) =I_output_time(j) + j*Q_output_time(j);
   r(j) = sqrt( (abs(I_output_time(j)))^2 + (abs(Q_output_time(j)))^2);
end

% normalize and compute rms level
rms = sqrt( mean( r.*r ) );
row = r(1:Ns)/rms;

%plot(20*log10(row),'g')