function myEqualizer

randn('seed', 0) ;
rand('seed', 0) ;


%Variables%
NoOfData = 8000 ;				% Set no of data points used for training
Order = 15 ;					% Set the adaptive filter order
Mu = 0.01 ;					% Set the step-size constant
next=1;

sizeOfIn=16;


x=complex(randn(NoOfData,1),randn(NoOfData,1)); % Input assumed to be white
h=complex(rand(Order, 1),rand(Order, 1));	% System picked randomly
d = filter(h, 1, x) ;				% Generate output (desired signal)


w=complex(zeros(Order+1,1),zeros(Order+1,1));

e = LMS_adaptive(sizeOfIn,NoOfData,Order,d,x,h,w);

% Plot results
%figure ;
plot(20*log10(abs(e))) ;

title('Learning Curve') ;
xlabel('Iteration Number') ;
ylabel('Output Estimation Error in dB') ;

return;

function e = LMS_adaptive( sizeOfIn,NoOfData,Order,d,x,h,w)

%LMS Adaptation
Mu = 0.01;

e=complex(0,0);

y=complex(zeros(NoOfData,1),zeros(NoOfData,1));

yI=0;yQ=0;eI=0;eQ=0;wI=0;wQ=0;xI=0;xQ=0;

in=complex(zeros(sizeOfIn,1),zeros(sizeOfIn,1));


if 0

for  n  = sizeOfIn : NoOfData

     in=x(n:-1:n-sizeOfIn+1) ;

     wI=real(w);

     wQ=imag(w);

     xQ=imag(in);

     xI=real(in);

     dI=real(d(n));

     dQ=imag(d(n));   


     y(n)=(wI'*xI + wQ'*xQ) + (wI'*xQ - wQ'*xI)*i;       

     yI=real(y(n));

     yQ=imag(y(n));

   

     %Error Calculation%

     e(n)=(dI-yI) + (dQ-yQ)*i;



     %Update Taps%

     eI=real(e(n));

     eQ=imag(e(n));

     w=(wI + Mu* ( eI*xI + eQ*xQ ))  +  (wQ + Mu* ( eI*xQ - eQ*xI))*i;

   

end ;
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

if 1
w=complex(zeros(Order,1),zeros(Order,1));    
for n = Order : NoOfData
	D = x(n:-1:n-Order+1) ;
	d_hat(n) = w'*D ;
	e(n) = d(n) - d_hat(n) ;
	w = w + Mu*conj(e(n))*D ;
	w_err(n) = norm(h - w) ;
end ;
end

return;