Plotting Theoretical Error Rates :: Performance Evaluation (Communications Toolbox)

Communications Toolbox

Plotting Theoretical Error Rates

The example below uses the bercoding function to compute upper bounds on bit error rates for convolutional coding with a soft-decision decoder. The data used for the generator and distance spectrum are from [5] and [2], respectively.

coderate = 1/4; % Code rate
% Create a structure dspec with information about distance spectrum.
dspec.dfree = 10; % Minimum free distance of code
dspec.weight = [1 0 4 0 12 0 32 0 80 0 192 0 448 0 1024 ...
   0 2304 0 5120 0]; % Distance spectrum of code
EbNo = 3:0.5:8;
berbound = bercoding(EbNo,'conv','soft',coderate,dspec);
semilogy(EbNo,berbound) % Plot the results.
xlabel('E_b/N_0 (dB)'); ylabel('Upper Bound on BER');
title('Theoretical Bound on BER for Convolutional Coding');
grid on;

The example produces the following plot.

Theoretical Performance Results

Comparing Theoretical and Empirical Error Rates