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% File: ARpredictor.m 
% ------------------- 
% This is the main file of Wiener predictor with Autoregression Model. 
 
function ARpredictor(a_vector, L, sigma_sqr) 
% a_vector: the parameters a(i) i = 1, ..., p of AR model in vector form 
%       also with the order (or length) of the model p = length(a_vector) 
% sigma_sqr: variance of white Gaussian noise 
% L: number of signal samples of s(n) 
ARpredictor_SeqGen(a_vector, sigma_sqr, L); % generate w(n) and s(n) 
ARpredictor_Core(); % compute autocorrelation matrix Rss 
 
load ARpredictor_SeqGen.mat; 
load ARpredictor_Core.mat; 
 
N = length(a_vector); 
mse_a_vector = 0; 
for i = 1: N 
    mse_a_vector = mse_a_vector + (a_vector(i) - a_vector_ass(i)) * (a_vector(i) - a_vector_ass(i)); 
end 
mse_a_vector = mse_a_vector / N; 
 
mse_ar_pow = (sigma_sqr - AR_MSE_ass) * (sigma_sqr - AR_MSE_ass); 
 
sprintf('mse_avector: %f \nmse_arpow: %f', mse_a_vector, mse_ar_pow) 
 
savefile = 'ARpredictor.mat'; 
save(savefile, 'mse_a_vector', 'mse_ar_pow'); 
 
clear;