📄 automc.m
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function [recm,h,recm1,recm2,mmse,ser]=automc(snr,LEN,CC,Qq)% --------------------------------------------------------------% function [recm,h,recm1,recm2,mmse,ser]=automc(snr,LEN,CC,Qq)% --------------------------------------------------------------%% Monte Carlo simulations of the CBEA algorithm.% % snr : the SNR of the additive noise% LEN : the number of symbols of the output signals% CC : the length of the cepstrum window% Qq : the length of the autocorrelation window%% recm : the vector of reconstructed channels h(n)% h : the actual channel% recm1: the vector of reconstructed channels h1(n)% recm2: the vector of reconstructed channels h2(n)%% Author: H. Pozidis, September 23, 1998% --------------------------------------------------------------NI=50; dim=2;Lmin=5; Lmax=7; % Example lengths for the intermediate sequencesslen=LEN; no_seg=LEN/slen;recm=[]; recm1=[]; recm2=[];h=ray2(0.11,.7,.8,6); Lh=length(h); h=reshape(h,2,Lh/2);%h=ray3(0.12,[0.4 1.2],[0.6 0.8],6); Lh=length(h); h=reshape(h,2,Lh/2);h1=h(1:2:Lh); h2=h(2:2:Lh);for k=1:NI randn; seeds(k,1)=randn('seed'); randn; seeds(k,2)=randn('seed'); randn; gaus(k,1)=randn('seed'); randn; gaus(k,2)=randn('seed'); randn; gaus(k,3)=randn('seed'); randn; gaus(k,4)=randn('seed');endfor jk=1:NI s1=seeds(jk,1); s2=seeds(jk,2); s3=gaus(jk,1); s4=gaus(jk,2); s5=gaus(jk,3); s6=gaus(jk,4); s=PAM(dim,LEN,1,s1); s=s(:);% s=qam(dim,LEN,1,s1,s2); s=s(:); x1=conv(s,h1); x2=conv(s,h2); if (abs(sum(imag(s))) > 0.001) [rx1,ix1,rx2,ix2] = addnoise(x1,x2,snr,s3,s4,s5,s6); x1=rx1+sqrt(-1)*ix1; x2=rx2+sqrt(-1)*ix2; else [x1,x2]=real_noise(x1,x2,snr,s3,s4); end for kk=1:no_seg u1=x1(slen*(kk-1)+1:slen*kk); u2=x2(slen*(kk-1)+1:slen*kk); r=cbea(snr,slen,CC,Qq,u1,u2,h1,h2,Lmin,Lmax); % for QAM inputs HR(kk,:)=r(:).'; end if (no_seg == 1) recm(jk,:)=HR; else recm(jk,:)=mean(HR); end [ues,mmse(jk),sym,er,ser(jk)]=calc_fse_SER('p',dim,h(:),recm(jk,21:32),snr,32,jk); recm1(jk,:)=recm(jk,1:2:length(recm(jk,:))); recm2(jk,:)=recm(jk,2:2:length(recm(jk,:))); disp(jk);end
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