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BER Analyzer

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(joined March 2014)
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Attached is a BER Analyzer demonstrating how important post processing could be handled by a Matlab component.

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    • #26436
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      Damian Marek
      Participant

      Attached is a BER Analyzer demonstrating how important post processing could be handled by a Matlab component.

    • #26439
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      Damian Marek
      Participant

      Matlab code follows:

      %%% BER Analyzer code is not 100% same as OptiSystem’s %%%

      %%% Gathering data from input port %%%
      binary = InputPort1.Sequence;
      electrical = InputPort2.Sampled.Signal + InputPort2.Noise.Signal;
      time = InputPort2.Sampled.Time;

      %%% timeSpace is the difference in time between samples %%%
      timeSpace = time(2) – time(1);
      samples = length(electrical)/length(binary);

      %%% assign initial values and threshold level (currently 50%) %%%
      timeArray(1) = 0;
      spaceIndex = 0;
      markIndex = 0;
      elecIndex = 0;
      threshold = 0.5;

      %%% finds all 0’s and 1’s, and groups all their samples into separate arrays %%%
      for i = 1:length(binary)
      if (binary(i) == 0)
      spaceIndex = spaceIndex + 1;
      for j = 1:samples
      spaceArray(spaceIndex, j) = electrical(elecIndex+j);
      end;
      else
      markIndex = markIndex + 1;
      for j = 1:samples
      markArray(markIndex, j) = electrical(elecIndex+j);
      end;
      end;
      elecIndex = elecIndex + samples;
      end;

      %%% creates the timeArray or x-axis of the graphs %%%
      for i = 2:samples
      timeArray(i) = timeArray(i-1) + timeSpace;
      end;

      %%% plots the Eye Diagram %%%
      figure
      plot(timeArray, markArray, timeArray, spaceArray);
      title(‘Eye Diagram’,’FontSize’,16);
      pause(3);

      %%% calculates average amplitude of 1’s and 0’s, their %%%
      %%% standard deviations, Q factor, and eye height %%%
      for j = 1:samples
      for i = 1:markIndex
      temp1(i) = markArray(i,j);
      end
      u1(j) = sum(temp1)/markIndex;
      std1(j) = std(temp1);
      for i = 1:spaceIndex
      temp0(i) = spaceArray(i,j);
      end;
      u0(j) = sum(temp0)/spaceIndex;
      std0(j) = std(temp0);
      if (std1(j)+std0(j) == 0)
      Q(j) = 0;
      else
      Q(j) = abs(u1(j)-u0(j))/(std1(j)+std0(j));
      eyeHeight(j) = (u1(j)-3*std1(j)^2) – (u0(j)+3*std0(j)^2);
      end;
      end

      %%% plots the Q-factor %%%
      figure
      plot(timeArray, Q);
      title(‘Q-factor’,’FontSize’,16);
      pause(3);

      %%% plots the Eye Height%%%
      figure
      plot(timeArray, eyeHeight);
      title(‘Eye Height’,’FontSize’,16);
      pause(3);

      %%% calculates the threshold, probability of 1’s and 0’s, and the BER %%%
      for j = 1:samples
      S(j) = (mean(markArray(:,j))-std1(j) + mean(spaceArray(:,j))+std0(j))/2;
      if (std0(j)==0)
      Pe0(j) = 0;
      else
      Pe0(j) = 1/2*erfc(abs(((S(j)-u0(j))/(sqrt(2)*std0(j)))));
      end;
      if (std1(j)==0)
      Pe1(j) = 0;
      else
      Pe1(j) = 1/2*erfc(abs(((u1(j)-S(j))/(sqrt(2)*std1(j)))));
      end;
      Pe(j) = log10(spaceIndex/(spaceIndex+markIndex)*Pe0(j) + markIndex/(markIndex+spaceIndex)*Pe1(j));
      end

      %%% plots the threshold %%%
      figure
      plot(timeArray, S);
      title(‘Threshold’,’FontSize’,16);
      pause(3);

      %%% plots the BER using Gaussian approx. %%%
      figure
      plot(timeArray, Pe);
      title(‘Log of Min BER using Gaussian approx.’,’FontSize’,16);
      pause(3);

      %%% calculates and plots the BER from the Q-factor %%%
      PeWC = log10(1/2*erfc(Q/sqrt(2)));
      figure
      plot(timeArray, PeWC);
      title(‘Log of Min BER from Q’,’FontSize’,16);
      pause(3);

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