autocorr_sim.m

3gpp2信道模型,8个matlab源代码

function autocorr_sim=autocorr_sim(K,N,num_trials)
%    AUTOCORR_SIM(M,K,N,NUM_TRAIL,FDTS)
%    usage: autocorr_sim_1(2e4,3e4,1e2)
%    AUTOCORR_SIM simulate the autocorrelation of the proposed fading channel model
%    M is the number of sum_of_sinusoids
%    K is the autocorrelation sequence length of interest 
%    N is the length of each channel realization
%    num_trials is the number of random channel realizations
%    fdTs is the normalized sampling period
%    updated by HYM on 08/23/2004
autocorr_sim_matrix=zeros(num_trials,K);    % store the autocorrelation of all num_trials random channel realizations
for trial=1:num_trials
%     psi=2*pi*(rand(1,M)-0.5);               % generate the independent and uniformly distributed parameters defined in the model
%     theta=2*pi*(rand(1,1)-0.5);
%     alpha=(2*pi.*[1:1:M]-pi+theta)./(4*M);
%     phi=2*pi*(rand(1,1)-0.5);
%     channel_realization=zeros(1,N);
%     for n=1:N
%         channel_realization(n)=2/sqrt(M).*complex(sum(cos(psi).*cos(2*pi*n*fdTs.*cos(alpha)+phi)),sum(sin(psi).*cos(2*pi*n*fdTs.*cos(alpha)+phi)));
%     end
    ch=mimo_channel(1, 1, 0, 900, 30, 250e3, N,1);
    channel_realization=ch(:);
    temp=xcorr(real(channel_realization),'unbias');
    autocorr_sim_matrix(trial,:)=temp(N:N+K-1);    % need only K autocorrelation values  
end
fd=900*30*1e9/3600/3e8;
Ts=1/250e3;
fdTs=fd*Ts;
autocorr_sim=mean(autocorr_sim_matrix);
reff=besselj(0,2*pi*fdTs*[0:K-1]);
plot(fdTs.*[0:1:K-1],autocorr_sim,'b-')
hold on
plot(fdTs.*[0:1:K-1],0.5*reff,'r:');