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])