bercurve_cv_soft2.m

Computes BER v EbNo curve for convolutional encoding / soft decision Viterbi decoding scheme assum

% Computes BER v EbNo curve for convolutional encoding / soft decision 
% Viterbi decoding scheme assuming BPSK 
% Brute force Monte Carlo approach is unsatisfactory (takes too long) 
% to find the BER curve.
% The computation uses a quasi-analytic (QA) technique that relies on the
% estimation (approximate one) of the information-bits Weight Enumerating 
% Function (WEF) using
% a simulation of the convolutional encoder
% Once the WEF is estimated, the analystic formula for the BER is used.
%
% For more info on the QA, see for example:
% "Error Control Coding", Lin & Costello, 2nd edition p 530-535
% "Simulation of Communication Systems", Jeruchim et al, 1992, p 619

% (c) Dr B. Gremont, 2007
clear
clc
codeselect=1;

switch codeselect
    case 1
        Gpoly=[27 31];
    case 2
        Gpoly=[6 2 6; 2 4 4];
    case 3
        Gpoly=[4 2 6; 1 4 7];
    case 4
        Gpoly=[60 30 70; 14 40 74];
    otherwise
        Gpoly=[561 753];
end

%===============================================================
% Find key descriptors of the convolutional code
%===============================================================

[K, M, nu, n, k, coderate, StateTable]=getcodeparameters(Gpoly);
% StateTable
%===============================================================
% Estimate the information WEF (Weight Enumerating Function)
%===============================================================
[Bd, dfree]=EstimateBitWEF(Gpoly);
figure(1)
bar(Bd)
title('Estimated information bit WEF')
xlabel('d')
ylabel('Bd')

%===============================================================
% Compute BER v EbNo for unqnatized & quantized VA decoders
%===============================================================
EbNodBvals=0.5:0.5:15;
% compute BER for unquantized soft decision VA decoding
Pb=ComputeBerSoftDecisionUnquantized(EbNodBvals, Bd, coderate);
figure(2)
if ishold, hold,end
semilogy(EbNodBvals,Pb,'r') % plots results
hold
% compute BER for quantized soft decision VA deciding
% Nb; if Q=2 then we get hard quantized VA decoding results
% we assume uniform quantization with Q levels regularly spaced
% between 0 and 1
Qvals=2.^(1:5); % No of quantization levels for quantized sift decision VA deoding
figure(2)
for i=1:length(Qvals)
    Q=Qvals(i);
    Pb=ComputeBerSoftDecisionQuantized(EbNodBvals, Q, Bd, coderate);
    if Q==2,
        semilogy(EbNodBvals,Pb,'b') % plots results
    else
        semilogy(EbNodBvals,Pb,'k') % plots results
    end
    figure(gcf)
    pause(1)
end    
axis([2 15 10.^(-12) 10.^(-2)])
grid
figure(gcf)