线性MMSE均衡器的matlab源码_适应信道衰减不是很剧烈的情况_matlab_信道均衡

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; fd=1; % doppler frequency fs=2560; % sample frequency Ns=fs/20; 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) r=rayleigh_new(fd,fs,Ns); y=filter(r,1,x); d=awgn(y,20,'measured'); w=complex(zeros(Order+1,1),zeros(Order+1,1)); 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)); %LMS Adaptation 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 ; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if 0 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*e(n)*D ; w_err(n) = norm(h - w) ; end ; end % Plot results figure ; plot(20*log10(abs(e))) ; title('Learning Curve') ; xlabel('Iteration Number') ; ylabel('Output Estimation Error in dB') ;