📄 run_lms_eq.m
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function lms_eq(rp)
str_update=['Running -> eigenvalue spread: ' num2str(rp.T) ' results file: ' num2str(rp.name)];
disp(str_update);
% Section 9.7, Adaptive Filter Theory, 3rd edition
% Adaptive equalization
seed = 0:(rp.Nruns-1);
h = [0.5*(1+cos((2*pi/rp.T)*((1:3) - 2)))];
Npred = rp.Ndata - 1 - rp.p;
E = zeros(Npred, rp.Nruns);
Wx = zeros(rp.p, Npred);
for iter = 1:rp.Nruns,
rand( 'seed', seed(iter));
randn('seed', seed(iter));
a = 2*(rand(rp.Ndata, 1) > 0.5) - 1;
Xi = conv(a, h);
% account for shift in input sequence introduced by
% convolution with h and offset 1 addressing in MATLAB
Xi = Xi((length(h)+1):length(Xi));
Xi = Xi + sqrt(rp.var_v)*randn(length(Xi), 1);
[Xi, Y] = mksstrndata(Xi, rp.p);
Y = a((rp.tau+1) : rp.Ndata);
lms_eq;
E(:, iter) = e;
Wx = Wx + Wo;
end; % for iter
Wx = Wx / rp.Nruns;
eval(['save ' rp.name])
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