DetectionResults.m

% 1) This file gives a plot of the probability of correct identification
% 'pci' in terms of the SNR while considering a specific scenario

% 2) Three possible scenarios : 'HOC only', 'HOC + non-denoised HOM',
% and 'HOC + denoised HOM'

% 3) Before running this file, you have to load all the variables generated
% by 'GenDataset.m'

HOSth = { % Theoretical HOS values, cf. Table 1 of the paper

'   '  'B-PSK' 'Q-PSK' '8-PSK' '4-ASK' '8-ASK' '16-QAM'    ;
'M40'    1      -1       0       1.64    1.77    -0.67     ;
'M41'    1       0       0       1.64    1.77     0        ;
'M42'    1       1       1       1.64    1.77     1.32     ;
'M60'    1       0       0       2.92    3.62     0        ;
'M61'    1      -1       0       2.92    3.62    -1.32     ;
'M62'    1       0       0       2.92    3.62     0        ;
'M63'    1       1       1       2.92    3.62     1.96     ;
'M84'    1       1       1       5.25    7.92     3.12     ;
'C40'   -2      -1       0      -1.36   -1.42    -0.68     ;
'C41'   -2       0       0      -1.36   -1.42     0        ;
'C42'   -2      -1      -1      -1.36   -1.42    -0.68     ;
'C60'    16      0       0       8.32    7.19     0        ;
'C61'    16      4       0       8.32    7.19     2.08     ;
'C62'    16      0       0       8.32    7.19     0        ;
'C63'    16      4       4       8.32    7.19     2.08     ;

};

HOSth = cell2mat(HOSth(2:end,2:end));

lSNR = length(SNR);
lM = length(M);
lMMC = lM*lMC;

stat = zeros(4+Nt,lSNR*lMMC);

count = 1;
for cptSNR=1:lSNR
    for cptM=1:lM
        for cptMC=1:lMC           
            stat(1,count) = SNR(cptSNR);
            stat(2,count) = M(cptM);
            count = count+1;      
        end
    end
end

% HOSth = HOSth(9:end,:);        % Uncomment for the scenario 'HOC only'

for cptNt=1:Nt

    % The scenario 'HOC only':
    % mat_HOS_cptNt = mat_HOS(15:end,:,cptNt);

    % The scenario 'HOC + non-denoised HOM':
    % mat_HOS_cptNt = mat_HOS([1 3 5 6 8 10 12 14 15 16 17 18 19 20 21],:,cptNt);

    % The scenario 'HOC + denoised HOM':
    mat_HOS_cptNt = mat_HOS([1 2 4 6 7 9 11 13 15 16 17 18 19 20 21],:,cptNt);

    [~,resUz] = min(pdist2(HOSth',mat_HOS_cptNt','euclidean')); % MD classifier
    stat(2+cptNt,:)=resUz;

end

if Nt==1
   stat(4,:) = stat(3,:);
else
   stat(3+Nt,:) = mode(stat(3:2+Nt,:)); % frequency (number of occurrences)
end
stat(4+Nt,:) = double(stat(2,:)==stat(3+Nt,:));

pci = zeros(1,lSNR);
end_ = 0;
for cptSNR=1:lSNR
    beg_ = end_+1;
    end_ = beg_+lMMC-1;
    pci(cptSNR) = sum(stat(4+Nt,beg_:end_))/lMMC;
end
figure
plot(SNR,pci)