## 4 Results and Discussion
### AMC Phase Results  
Table 6 illustrates the MLP-based classification results for modulation type recognition. The number of neurons for each layer is 3, 30, and 4, respectively. The results show that in case of no jamming, the average AMC accuracy is 87%. In the presence of jamming signals, the classification performance is degraded locally, about 3%, 16%, and 36% for CWI, CI, and MCWI, respectively. Each AMC classifier refers to a specific class of the received signal. AMC1, AMC2, AMC3, and AMC4, are respectively referred to Sol, Sol + CWI, Sol + MCWI, and Sol + CI.  
Table 7 demonstrates the effect of GA-based feature selection on the second phase of classification to recognize the modulation types. As is shown, MLP + GA can precisely classify the four modulation types per each received signals using only one feature with a precision of 90.83%, 86.66%, 61.66% and 76.67% for AMC1 to AMC4 respectively.  
Table 8 illustrates the effect of PCA-based feature selection on classifying the modulation types. As is shown, MLP + PCA performs efficiently using only one feature for AMC1 to 4 with an accuracy of 86%, 89.77%, 51.11%, and 67.66%, respectively. The learning process is accelerated using PCA because the classifier is trained with fewer features.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **BS = 1** & **BS = 100** & **Full-Batch** \\ \hline
**AMC1** & 87\% & 57\% & 37.25\% \\
**AMC2** & 84.25\% & 43\% & 27\% \\
**AMC3** & 48\% & 47.50\% & 49\% \\
**AMC4** & 71\% & 65\% & 20\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: AMC results based on MLP at different HLN and BS.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NoF** & **AMC1** & **AMC2** & **AMC3** & **AMC4** \\ \hline
**2** & 81.33\% & 85.66\% & 50.33\% & 66.66\% \\
**1** & 86\% & 89.77\% & 51.11\% & 67.66\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of GA-based feature selection on the proposed MLP using HLN = 30 for AMC phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NoF** & **BS = 1** & **BS = 100** & **BS = 500** & **BS = 1500** & **Full-Batch** \\ \hline
2 & 94.41\% & 85.46\% & 80.04\% & 52.77\% & 48.46\% \\
5 & 99.81\% & 98.39\% & 87.84\% & 72.63\% & 49.65\% \\
8 & 99.97\% & 99.76\% & 96.45\% & 81.53\% & 50.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of GA-based feature selection on the designed MLP’s performance (\(HLN=30\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NoF** & **AMC1** & **AMC2** & **AMC3** & **AMC4** \\ \hline
**2** & 88.58\% & 81.67\% & 43.33\% & 70\% \\
**1** & 90.83\% & 86.66\% & 61.66\% & 76.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of PCA-based feature selection on the proposed MLP using HLN = 30 for AMC phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**NoF** & **B = 1** & **BS = 100** & **BS = 500** & **BS = 1500** & **Full-Batch** \\ \hline
2 & 94.41\% & 85.46\% & 80.04\% & 52.77\% & 48.46\% \\
5 & 99.81\% & 98.39\% & 87.84\% & 72.63\% & 49.65\% \\
8 & 99.97\% & 99.76\% & 96.45\% & 81.53\% & 50.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of PCA-based feature selection on the designed MLP’s performance (\(HLN=30\)).