?? rayleigh_fading.mht
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Date: Sat, 21 Jun 2008 15:54:18 +0330
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<BODY><PRE>% Program to simulate Rayleigh fading using a p-th order =
autoregressive model AR(p) according to=20
% Baddour's work: "Autoregressive modeling for fading channel =
simulation", IEEE Transaction on Wireless Communications, July 2005.
function [chann]=3DRayleigh_fading(P,M,fm,fs,epselonn)
% P: AR model order
% M: number of samples
% fd: maximum doppler frequency in Hz
% fs: Symbol frequency in ksps
% epselonn: added bias, depends on the Doppler rate, see the paper =
"Autoregressive modeling for fading channel simulation".=20
=20
% usage: Rayleigh_fading(100,10000,150,3,0.00000001)
=20
=20
=
%------------------------------------------------------------------------=
------------------------------------------------- =20
=20
for p=3D1:P+1
vector_corr(p)=3Dbesselj(0,2*pi*fm*(p-1)/(fs*1000)); % Bessel =
autocorrelation function according to Jakes' model
end
=20
=
auto_correaltion_matrix=3Dtoeplitz(vector_corr(1:P))+eye(P)*epselonn; % =
adding a small bias, epselonn, to the autocorrelation matrix to overcome =
the ill conditioning of Yule-Walker equations
=
AR_parameters=3D-inv(auto_correaltion_matrix)*vector_corr(2:P+1)'; % =
Solving the Yule-Walker equations to obtain the model parameters
=
segma_u=3Dauto_correaltion_matrix(1,1)+vector_corr(2:P+1)*AR_parameters;
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KKK=3D2000;
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h=3Dfilter(1,[1 =
AR_parameters.'],wgn(M+KKK,1,10*log10(segma_u),'complex')); % Use the =
function Filter to generate the channel coefficients
chann=3Dh(KKK+1:end,:); % Ignore the first KKK samples
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=
%------------------------------------------------------------------------=
-------------------------------------------------=20
=20
</PRE></BODY></HTML>
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