QAM.rar_The Signal_adaptive qam_qam

function num_errors=equalizer_QAM_Ld(D,equalizer_length,k,SNR,channel) %k=1,2,3,4 equivalent to 4,16,64,256-QAM respectively %SNR at output of channel %D: delay n=50000; mu=0.4; epxilon=10^-6; training = 1000; N=training + n; % total number of symbols s=zeros(1,N); switch k case 1 signal_avg_energy = sqrt(2); case 2 signal_avg_energy = sqrt(10); case 3 signal_avg_energy = sqrt(42); case 4 signal_avg_energy = sqrt(170); end sigma_v_dd = sqrt(signal_avg_energy^2*norm(channel)^2/db2pow(SNR)); %noise noise=zeros(1,N); noise(1:N)=sigma_v_dd*(randn(1,N)+1i*randn(1,N)); %creat QAM data sequence [s_r,s_i]=QAM(k,N); s(1:N)=s_r + 1i*s_i; train_data(D+1:training+D)=s(1:training); signal_output=filter(channel,1,s); %signal to be sent signal_received=signal_output+noise; % Equalizer initialization w = zeros(equalizer_length,1); num_errors=0; % Training period u = zeros(1,equalizer_length); for i=1:training+D u=[signal_received(i) u(1:end-1)]; signal_evaluated= u*w; signal_desired=train_data(i); e=signal_desired- signal_evaluated; w=w+mu*e*u'/(norm(u)^2+epxilon); end % decision directed for i = training+D+1:N u = [signal_received(i) u(1:end-1)]; signal_evaluated= u*w; switch k case 1 check_s=decision_QAM_4(signal_evaluated); case 2 check_s=decision_QAM_16(signal_evaluated); case 3 check_s=decision_QAM_64(signal_evaluated); case 4 check_s=decision_QAM_256(signal_evaluated); end if (check_s~=s(i-D)) num_errors = num_errors + 1; end end