randn_bl.m

source Matlab traites the UWB

%
% function [y,w] = randn_bl(ALPHA,T,Nt,B);
%
% ALPHA: number of symbols for which we need samples
% Nt:    number of samples per symbols
% T:     duration of a symbol
% B:     bandwidth of the bandlimited Gaussian noise
% S2:    Variance of the Gaussian random variable
%
% Example: fs=10e9; T = 1e-6; Nt = fs*T; B = 1e9;
%          [y,w]=randn_bl(100,T,Nt,B);
%
function [y,w] = randn_bl(ALPHA,T,Nt,B,S2);

N = ALPHA*Nt;

% $$$ fprintf('ALPHA = %d\n',ALPHA);
% $$$ fprintf('Nt    = %d\n',Nt);
% $$$ fprintf('T     = %.2e\n',T);
% $$$ fprintf('B     = %.2e\n',B);
% $$$ fprintf('2*N = %d, 2*ALPHA*B*T = %d\n',2*N,2*ALPHA*B*T);

% Step A1
k = 0:1:2*N-1;
H = sqrt(2*N*S2*df_psd(ALPHA,T,Nt,B));

% Step B2
X = randn(2,2*N);
S = H.*(X(1,:)+j*X(2,:));

clear H; %free memory
clear X; %free memory

% Step B3
s = ifft(S);

clear S; %free memory

y = real(s(1:N));
w = imag(s(1:N));