rayleigh_fading.txt

Rayleigh 信道仿真模型 参考"Autoregressive modeling for fading channel simulation", IEEE Transaction on Wirel

% Program to simulate Rayleigh fading using a p-th order autoregressive model AR(p) according to 
% Baddour's work: "Autoregressive modeling for fading channel simulation", IEEE Transaction on Wireless Communications, July 2005. 

function [chann]=Rayleigh_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". 
    
    % usage: Rayleigh_fading(100,10000,150,3,0.00000001) 
    
    
    %-------------------------------------------------------------------------------------------------------------------------    
      
        for p=1:P+1 
          vector_corr(p)=besselj(0,2*pi*fm*(p-1)/(fs*1000)); % Bessel autocorrelation function according to Jakes' model 
        end 
        
        auto_correaltion_matrix=toeplitz(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=-inv(auto_correaltion_matrix)*vector_corr(2:P+1)'; % Solving the Yule-Walker equations to obtain the model parameters 
        segma_u=auto_correaltion_matrix(1,1)+vector_corr(2:P+1)*AR_parameters; 
        
        KKK=2000; 
    
        h=filter(1,[1 AR_parameters.'],wgn(M+KKK,1,10*log10(segma_u),'complex')); % Use the function Filter to generate the channel coefficients 
        chann=h(KKK+1:end,:);  % Ignore the first KKK samples 
          
    %-------------------------------------------------------------------------------------------------------------------------