matlab_up.rar_Channel_Equalizer_IDEAL_channel equalizer_laser_pi

% program that performs rls or lms algorithm % can be used for channel equalization % or for channel identification function f=rls_lms(gcurr,ffcurr,over,lms_rls,eq_length,dec_delay,param1,param2); % gcurr : input (possibly oversampled) data, % row vector of length L % ffcurr : desired decision points, % row vector of length L/over (see below) % over : 1 --> symbol spaced, 2 --> fractionally spaced % For LTE can also be 8. % lms_rls : 1 --> lms, 2 --> rls % eq_length : equalizer filter length % dec_delay : equalizer decision delay % param1 : if lms - lte --> m of lms (default=0.375) % if lms - dfe --> m1 of lms for feedforward part % (default=0.575) % if rls --> delta, initialization parameter % for P matrix (default=0.001) % param2 : if lms - dfe --> m2 of lms for feedback part % (default=0.0175) % if rls --> lamda, forgetting factor (default=0.999) % f : output filter coefficients training=min(length(ffcurr),length(gcurr)); g=zeros(eq_length,1)'; if lms_rls==1 if nargin < 7 m=0.375; else m=param1; end else if nargin < 7 delta=0.001; else delta=param1; end if nargin < 8 lamda=0.999; else lamda=param2; end end w=zeros(1,eq_length); alg_error=[]; pe=(1/delta)*eye(eq_length); if lms_rls==1, for i=eq_length:over:over*training-10, if over==1, g=fliplr(gcurr(i-eq_length+1:i)); desired=ffcurr(i-dec_delay); end if over==2, g=fliplr(gcurr(i-eq_length+1:i)); des_pos=fix((i-dec_delay)/over)+rem((i-dec_delay),over); desired=ffcurr(des_pos); end; eq_out=w*g'; error=desired-eq_out; alg_error=[alg_error error^2]; norm=g*g'; w=w+(m*error*g)/norm; end; else for i=eq_length:over:over*training-10 if over==1, g=fliplr(gcurr(i-eq_length+1:i)); desired=ffcurr(i-dec_delay); end if over==2, g=fliplr(gcurr(i-eq_length+1:i)); des_pos=fix((i-dec_delay)/over)+rem((i-dec_delay),over); desired=ffcurr(des_pos); end; if over==8, g=fliplr(gcurr(i-eq_length+1:i)); des_pos=fix((i-dec_delay)/over)+1; desired=ffcurr(des_pos); end; eq_out=w*g'; error=desired-eq_out; alg_error=[alg_error error^2]; pi=pe*g'; ka=lamda+(g*pi); kapa=pi/ka; w=w+kapa'*error; pet=kapa*pi'; pe=(1/lamda)*(pe-pet); end end f=w; hold off plot(alg_error) pause return end