PCode.zip_Polar Coding；MATLAB_pdecode_极化码_极化码 matlab_极化码MATLAB

function plotPC_systematic_codechanging(N,K,channelstring,channelstate_range,MaxIterations) % last argument is optional % This is exactly the same as plotPC_systematic(), except for a % loss-of-one-argument that code is re-constructed -aka- frozenbits % are recomputed at every channel state, before the Monte-Carlo is run. % % Plot the performance (FER/BER) curve for (N,K) polar code, by % % 1. Re-constructing the code at a given design-state of the channel % (existing construction is restored at the end) % 2. Generating as many random messages % 3. Performing systematic-encoding on each sample % 4. SC-decoding extended to systematic-polar codes % 5. Repeat at most 'MaxIterations' times, and report the stats % ======================================================================= % Usage: plotPC_systematic_codechanging(N,K,channelstring,design_channelstate,channelstate_range,MaxIterations) % % N - Block length % K - Message length % % channelstring - 'AWGN' or 'BEC' or 'BSC' % % channelstate_range % - The range of channel state values at which code % performance (FER & BER) is to be simulated. % AWGN -> it is SNR:=Eb/N0 range in dB scale % BSC -> it is the range of p values % BEC -> it is the range of epsilon values % (will be ascending sorted immediately within, % to aid avoiding useless MonteCarlo iterations and also for plots) % % design_channelstate - The design-state of the channel, % at which the PCC should be run (frozen bit % indices are chosen) % MaxIterations - Maximum number of iterations to try, to get a % reasonable estimate of the BER/FER. It smartly stops % whenever 100 Frames are already received in error, where % the estimate is good enough. If we fail to get 100 frames % in error even after running MaxIterations, we stop running any higher quality. % % ********************************************************** % Results of FER, BER and channel_param_range are made available on command line % (so called 'base' workspace) % ********************************************************** % % Note: This routine, preserves the global structure PCparams. It changes % the structure but restores at the end. So subsequent calls to functions % pencode(),pdecode() etc all are not affected. % % @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ % WRITTEN BY: Harish Vangala, Emanuele Viterbo, and Yi Hong, % Dept of ECSE, Monash University, Australia. % % - Latest as on 2016-March-03 % - Available ONLINE for free: is.gd/polarcodes % - Freely distributed for educational and research purposes % @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ global PCparams; errorflag=0; if nargin==2 %Quick-test-run channelstring='AWGN'; design_channelstate=0; %dB channelstate_range = 0:1:2; %default SNR range to quickly see the o/p MaxIterations=10000; else if(nargin==5 || nargin==6) if nargin==5 MaxIterations=10000; end if (strcmpi(channelstring,'AWGN')||strcmpi(channelstring,'BEC')||strcmpi(channelstring,'BSC')) errorflag=0; else fprintf('\n\n Invalid Channel String "%s" Supplied! (It should be one of "AWGN","BEC" or "BSC" only) \n\n',channelstring); errorflag=1; end else errorflag=1; end end if(errorflag==1) fprintf('\n Usage: plotPC_systematic_codechanging(N,K,channelstring,channelstate_range,MaxIterations)\n'); fprintf('\n N - Blocklength'); fprintf('\n K - Message length (Rate = K/N)'); fprintf('\n channelstring - "AWGN" or "BEC" or "BSC"'); fprintf('\n channelstate_range - the range of parameters at which Monte-Carlo simulation to be run'); fprintf('\n MaxIterations - Maximum Monte-Carlo iterations to be run\n\n'); return; end PCparams1 = PCparams; %store the current % Save some typing chstr = channelstring; chrange = sort(channelstate_range); % *IMP* if they are in "out-of-order" xlab=[]; if(strcmpi(chstr,'AWGN')) xlab='Eb/N0'; paramstr='SNR (Eb/N0) in dB'; elseif(strcmpi(chstr,'BSC')) xlab='Transition probability "p" of BSC'; paramstr='p'; elseif(strcmpi(chstr,'BEC')) xlab='Erasure probability "\epsilon" of BEC'; paramstr='\epsilon'; end %%%%%%%%%%%%%%%%%% MCsize = MaxIterations; %Montecarlo size MinFrameErrors=100; MinIters=500; fprintf('\n * Max. Monte-Carlo Iterations = %d, ensuring %d frame errors, and a guaranteed minimum of %d iterations',MCsize,MinFrameErrors,MinIters); fprintf('\n * Polar Code *re-constructed* for every channel state of %s-%s (i.e. different code everytime) ',chstr,paramstr); global BER; global FER; BER = zeros(1,length(chrange)); FER = zeros(1,length(chrange)); fprintf('\n * Channel-states (%s-%s) to be run: \n\t ',chstr,paramstr); disp(chrange); for jj=1:length(chrange) % To ensure increasing quality of channels, % for a useful MonteCarlo stopping criteria. if(strcmpi(chstr,'AWGN')) j=jj; else j=length(chrange)-jj+1; end initPC(N,K,channelstring,chrange(j),1); %last optional argument '1' says "be silent" with no output at prompt. N=PCparams.N; %adjust in case of being not-a-power-of-2 K=PCparams.K; fprintf('\n Now running: %s-%s=%f [%d of %d] \n\t Iteration-Counter: %53d',chstr,paramstr,chrange(j),j,length(chrange),0); tt=tic(); for l=1:MCsize u=randi(2,K,1)-1; %Bernoulli(0.5) x=systematic_pencode(u); %Pass x via a simulated instance of the noisy channel, and grab the o/p y=OutputOfChannel(x,chstr,chrange(j)); uhat = systematic_pdecode(y,chstr,chrange(j)); nfails = sum(uhat ~= u); FER(j) = FER(j) + (nfails>0); BER(j) = BER(j) + nfails; if mod(l,20)==0 for iiiii=1:53 fprintf('\b'); end fprintf(' %7d ---- %7d FEs, %7d BEs found so far',l,FER(j),BER(j)); end if l>=MinIters && FER(j)>=MinFrameErrors %frame errors, sufficient to stop break; end end %%%%%%%EARLY STOPPING CRITERIA if(FER(j)<MinFrameErrors) FER(j) = FER(j)/l; BER(j) = BER(j)/(K*l); if(~strcmpi(chstr,'AWGN')) %For BEC and BSC, points in 'chrange' run in reverse order (increasing quality) FER(1:j-1) = 0; BER(1:j-1) = 0; else FER(j+1:end) = 0; BER(j+1:end) = 0; end fprintf('\n\t Total time taken: %.2f sec (%d samples), escaping the rest of the points due close to zero FER.',toc(tt),l); fprintf('\n\t (To have more successful points, you must increase the Max. MonteCarlo iterations\n\t passed as the last argument to plotPC(), and run again)\n'); break; else FER(j) = FER(j)/l; BER(j) = BER(j)/(K*l); fprintf('\n\t Total time taken: %.2f sec (%d samples)',toc(tt),l); end fprintf('\n'); end %%%%%%% Set the plot's default settings, including window size set(0,'DefaultAxesLineStyleOrder',{'-*','--*','-.*'}); scrsz = get(0,'ScreenSize'); figure('Position',[70 scrsz(4)/2-150 scrsz(3)/2 scrsz(4)/2]); %[left,bottom,width,height] hold all; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% subplot(211); semilogy(chrange,FER,'LineWidth',2,'MarkerSize',8); grid on; titlestr = sprintf('N=%d R=%.2f Systematic Polar code performance (reconstructed everytime)',N,(K/N)); title(titlestr,'fontsiz