GSLC with LMS/RLS adaptive algorithm

Question

I'm having some issues with my BSc final project, and I was wondering if you might help me out.

I'm trying to implement a digital detector based on GSLC scheme plus LMS and RLS algorithms (to compare each ones' performance, between others). Here is when my problem pops up. I've already successfully programmed the LMS part, however, when it comes to RLS, I'm stuck in a rut.

After looking up into some bibliography or through the Internet, I'm still lost. Here I attach the correspondent code:

 clear all
 clf
 N=6; % Number of antennas
 SNR=20; % SIgnal to noise ratio
 mu=.01; % LMS step
 niter=50000; % Number of iterations
 Npro=10; % Number of promedies

 lambda = 0.99; % Forgetting factor RLS
 delta = 0.001; % Initialization of P(0)

 % Scenario
 a=25/180*pi; % Desired signal
 b1=125/180*pi; % Interferring signal 1
 b2=-90/180*pi; % Interferring signal 2
 g=1; % Gain of our desired signal
 s2n=10.^(-SNR/10); 

 % Definition of signals
 sa=1/sqrt(N)*exp(j*pi*(0:N-1)*sin(a))';
 sb1=1/sqrt(N)*exp(j*pi*(0:N-1)*sin(b1))';
 sb2=1/sqrt(N)*exp(j*pi*(0:N-1)*sin(b2))';

 % Correlation matrices
 Rs=sa*sa';
 Rint=sb1*sb1'+sb2*sb2'+s2n*eye(N);
 Rx=Rs+Rint;

 % Projection and blocking matrices
 A=eye(N);
 A(:,1)=sa;

 % Gram-Schmidt algorithm
 Aort=zeros(size(A));
 Aort(:,1)=A(:,1)/sqrt(real((A(:,1)'*A(:,1))));
 for k1=2:N
    for k2=1:(k1-1)
     Aort(:,k1)=Aort(:,k1)+(Aort(:,k2)'*A(:,k1))*Aort(:,k2);
    end
    Aort(:,k1)=A(:,k1)-Aort(:,k1);
    Aort(:,k1)=Aort(:,k1)/sqrt(real((Aort(:,k1)'*Aort(:,k1))));
 end
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

 C=Aort(:,1); % Restrictions subspace
 B=Aort(:,2:end); % Restrictions' orthogonal subspace
 P_C=C*inv(C'*C)*C';
 P_C_ort=eye(N)-P_C;

 % Optimal solution
 wopt=inv(Rx)*C*inv(C'*inv(Rx)*C)*g;
 wq=sa;

 e_gslc_LMS=zeros(Npro,niter); % Error sequence LMS
 e_gslc_RLS=zeros(Npro,niter); % Error sequence RLS

 % GSLC algorithm
 for kk=1:Npro
    Xa = kron(sa,ones(1,niter)).*kron(ones(N,1),(sign(randn(1,niter))+1i*sign((randn(1,niter)))/sqrt(2)));
    Xb1 = kron(sb1,ones(1,niter)).*kron(ones(N,1),(sign(randn(1,niter))+1i*sign((randn(1,niter)))/sqrt(2)));
    Xb2 = kron(sb2,ones(1,niter)).*kron(ones(N,1),(sign(randn(1,niter))+1i*sign((randn(1,niter)))/sqrt(2)));
    X = Xa + Xb1 + Xb2 + (randn(size(Xa)) + 1i*randn(size(Xa)))/sqrt(2)*sqrt(s2n);

    w_gslc_LMS = zeros(N,niter);
    w_gslc_RLS = zeros(N,niter);
    xa = B'*X;

    wa_LMS = ones(N-1,niter);
    wa_RLS = ones(N-1,niter);

    P = inv(delta)*eye(N-1);

    for k = 2:niter
       y_LMS = X(:,k)'*w_gslc_LMS(:,k-1);
       y_RLS = X(:,k)'*w_gslc_RLS(:,k-1);

       % LMS
       wa_LMS(:,k) = wa_LMS(:,k-1) + mu*xa(:,k)*(X(:,k)'*wq - xa(:,k)'*wa_LMS(:,k-1));

       % RLS
       z = P*xa(:,k);
       alfa = X(:,k)'*wq - xa(:,k)'*wa_RLS(:,k-1); 
       ge = z/(lambda + xa(:,k)'*z);
       P = (P - ge*z')/lambda;
       wa_RLS(:,k) = wa_RLS(:,k-1) + alfa*ge; 
       w_gslc_LMS(:,k) = wq - B*wa_LMS(:,k);
       w_gslc_RLS(:,k) = wq - B*wa_RLS(:,k);
    end
    e_gslc_LMS(kk,:) = sum(abs(w_gslc_LMS - kron(wopt,ones(1,niter))).^2,1)/N;
    e_gslc_RLS(kk,:) = sum(abs(w_gslc_RLS - kron(wopt,ones(1,niter))).^2,1)/N;
 end

 figure(1)
 plot(10*log10(mean(e_gslc_LMS)),'b'), hold on
 plot(10*log10(mean(e_gslc_RLS)),'r')
 grid
 xlabel('Iterations')
 ylabel('dB')
 title('GSLC algorithm - Learning curve of LMS/RLS algorithms')

Please can you help me out to find out what is wrong? Any help is greatly appreciated!