%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Example: System Identification %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% In this example we have a typical system identification scenario. We want %
% to estimate the filter coefficients of an unknown system given by Wo. In %
% order to accomplish this task we use an adaptive filter with the same %
% number of coefficients, N, as the unkown system. The procedure is: %
% 1) Excitate both filters (the unknown and the adaptive) with the signal %
% x. In this case, x is chosen according to the BPSK constellation. %
% The variance of x is 1. %
% 2) Generate the desired signal, d = Wo' x + n, which is the output of the %
% unknown system considering some disturbance (noise) in the model. The %
% noise power is given by sigma_n2. %
% 3) Choose an adaptive filtering algorithm to govern the rules of coefficient %
% updating. %
% %
% Adaptive Algorithm used here: LRLS_priori %
% %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Definitions:
ensemble = 100; % number of realizations within the ensemble
K = 500; % number of iterations
H = [0.32+0.21*j,-0.3+0.7*j,0.5-0.8*j,0.2+0.5*j].';
Wo = real(H); % unknown system
sigma_n2 = 0.04; % noise power
lambda = 0.97; % forgetting factor
N = 4; % number of coefficients of the adaptive filter (= nSectionsLattice + 1)
epsilon = 1e-2; % small positive constant
% Initializing & Allocating memory:
ladder = zeros(N ,K+1,ensemble); % ladder coefficients of the algorithm
kappa = zeros(N ,K,ensemble); % reflection coefficients of the lattice algorithm
e = zeros(N+1,K,ensemble); % error matrix
MSE = zeros(K,ensemble); % MSE for each realization
MSEmin = zeros(K,ensemble); % MSE_min for each realization
% Computing:
for l=1:ensemble,
X = zeros(N,1); % input at a certain iteration (tapped delay line)
d = zeros(1,K); % desired signal
x = sign(randn(K,1)); % Creating the input signal - BPSK
sigma_x2 = var(x); % signal power = 1
n = sqrt(sigma_n2)*(randn(K,1)); % real noise
for k=1:K,
X = [x(k,1)
X(1:(N-1),1)]; % input signal (tapped delay line)
d(k) = (Wo'*X(:,1))+n(k); % desired signal
end
S = struct('lambda',lambda,'nSectionsLattice',(N-1),'epsilon',epsilon);
[ladder(:,:,l),kappa(:,:,l),e(:,:,l)] = LRLS_priori(d,transpose(x),S); %%% Algorithm 7.4
MSE(:,l) = MSE(:,l)+( (abs(e(N+1,:,l))).^2 ).';
MSEmin(:,l) = MSEmin(:,l)+(abs(n(:))).^2;
end
% Averaging:
ladder_av = sum(ladder,3)/ensemble;
kappa_av = sum(kappa,3)/ensemble;
MSE_av = sum(MSE,2)/ensemble;
MSEmin_av = sum(MSEmin,2)/ensemble;
% Plotting:
figure,
plot(1:K,10*log10(MSE_av),'-k');
title('Learning Curve for MSE (a priori error)');
xlabel('Number of iterations, k'); ylabel('MSE [dB]');
figure,
plot(1:K,10*log10(MSEmin_av),'-k');
title('Learning Curve for MSEmin');
xlabel('Number of iterations, k'); ylabel('MSEmin [dB]');
num = zeros(N+1,K); % estimate of Wo
for k=1:K
num(:,k) = latc2tf(-kappa_av(:,k),ladder_av(:,k));
end
figure,
plot(real(num(1,:))),...
title('Evolution of the 1st coefficient');
xlabel('Number of iterations, k'); ylabel('Coefficient');
