adaptive_MMSE_equalizer.rar_adaptive_equalizer_equalizer matlab_

% Training based channel equalization % adpative MMSE equalizer via LMS algorithm % % Copyright: Xiaohua(Edward) Li, Assistant Professor % Department of Electrical and Computer Engineering % State University of New York at Binghamton % http://ucesp.ws.binghamton.edu/~xli % June 2003 % T=3000; % total number of data M=2000; % total number of training symbols dB=25; % SNR in dB value %%%%%%%%% Simulate the Received noisy Signal %%%%%%%%%%% N=20; % smoothing length N+1 Lh=5; % channel length = Lh+1 P=round((N+Lh)/2); % equalization delay h=randn(1,Lh+1)+sqrt(-1)*randn(1,Lh+1); % channel (complex) h=h/norm(h); % normalize s=round(rand(1,T))*2-1; % QPSK or 4 QAM symbol sequence s=s+sqrt(-1)*(round(rand(1,T))*2-1); % generate received noisy signal x=filter(h,1,s); vn=randn(1,T)+sqrt(-1)*randn(1,T); % AWGN noise (complex) vn=vn/norm(vn)*10^(-dB/20)*norm(x); % adjust noise power with SNR dB value SNR=20*log10(norm(x)/norm(vn)) % Check SNR of the received samples x=x+vn; % received signal %%%%%%%%%%%%% adaptive MMSE equalizer estimation Lp=T-N; %% remove several first samples to avoid 0 or negative subscript X=zeros(N+1,Lp); % sample vectors (each column is a sample vector) for i=1:Lp X(:,i)=x(i+N:-1:i).'; end e=zeros(1,M-10); % used to save instant error f=zeros(N+1,1); % initial condition mu=0.001; % parameter to adjust convergence and steady error for i=1:M-10 e(i)=s(i+10+N-P)-f'*X(:,i+10); % instant error f=f+mu*conj(e(i))*X(:,i+10); % update equalizer estimation i_e=[i/10000 abs(e(i))] % output information end sb=f'*X; % estimate symbols (perform equalization) % calculate SER sb1=sb/norm(f); % scale the output sb1=sign(real(sb1))+sqrt(-1)*sign(imag(sb1)); % perform symbol detection start=7; % carefully find the corresponding begining point sb2=sb1-s(start+1:start+length(sb1)); % find error symbols SER=length(find(sb2~=0))/length(sb2) % calculate SER if 1 subplot(221), plot(s,'o'); % show the pattern of transmitted symbols grid,title('Transmitted symbols'); xlabel('Real'),ylabel('Image') axis([-2 2 -2 2]) subplot(222), plot(x,'o'); % show the pattern of received samples grid, title('Received samples'); xlabel('Real'), ylabel('Image') subplot(223), plot(sb,'o'); % show the pattern of the equalized symbols grid, title('Equalized symbols'), xlabel('Real'), ylabel('Image') subplot(224), plot(abs(e)); % show the convergence grid, title('Convergence'), xlabel('n'), ylabel('Error e(n)') end