## 5 Performance Analysis  
This section presents a comparative analysis of the reviewed studies to resolve the main study question, i.e., what are the appropriate AMC techniques for different modulation types in terms of overall performance by using different feature extraction methods and types of classifiers? Comparison of different classifiers is a complicated work. Researchers have developed various algorithms for AMC under different signals and conditions. Consequently, the performance of different classifiers cannot be compared when the modulation scheme is different. Furthermore, the feature set used by each system must be mentioned to achieve unbiased and reliable comparison. The benchmark of performance comparison is SNR, and accuracy rate is used to assess the classification system performance. Table 4 presents a lucid and comprehensive comparison of various works on AMC with their key features as the input and SNR and accuracy as the output parameters.  
As shown in Table 4, researchers have used various techniques in terms of feature selection, classifier types, and modulation set, and have evaluated their proposed methodologies under various SNRs. Three main conclusions can be derived as follows:  
1. Selection of appropriate features can improve system robustness towards noise effects and can be sensitive in terms of discriminating the modulation schemes, thereby enhancing the performance.
2. Features and classifier types with reduced complexity are crucial for enhancing the performance of classifiers.
3. Recognition is difficult when higher-order modulation types are used for higher-order QAM at low SNR.  
\begin{table}
\begin{tabular}{|p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}|} \hline
**Reference and Year** & **Classifier Types** & **Key Features** & **Modulation Set** & **SNR (dB)** & **PCC \%** & **Advantages** & **Disadvantages** \\ \hline
[99] & GP-SVM & HOC & 16 QAM, 64 QAM & 10 & 99.8 & \(\bullet\) & Performance enhancement & \(\bullet\) & Requires high SNR \\
[201] & GP-SVM & HOC & 16 QAM, 64 QAM & 10 & 99.8 & \(\bullet\) & Performance enhancement & \(\bullet\) & Limited to modulation types used \\ \hline
[29] & SVM with PSO & Combines features: spectral, higher-order statistical, and wavelet & 22 ASK, 4 ASK, 4 ASK, 4 ASK, 16 ASK, 2 PSK, 16 QAM, 32 QAM, 4 ASK, 4 ASK, 4 ASKASK, 4 ASKASK & \(\bullet\) & Incorporates large set of modulation schemes & \(\bullet\) & Uses many features Computationally complex \\ \hline
[28] & SVM & Spectral features & 2 ASK, 4 ASK, 2 PSK, 4 PSK & 0 & 81 & \(\bullet\) & Reduced complexity & \(\bullet\) & PCC increases when SNR is increased \\ \hline
[34] & Rough set with SVM & Instantaneous, HOC, cyclostationarity, wavelet & 22 ASK, 4 ASK, 8 ASK, 2 PSK, 4 PSK, 16 QAM & 22 ASK, 4 ASK, 8 ASK, 2 PSK, 4 PSK, 4 PSK,  
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\begin{table}
\begin{tabular}{|p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}|} \hline
**Reference and Year** & **Classifier** & **Key Features** & **Modulation Set** & **SNR (dB)** & **FCC \%** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{[71]} & \multirow{3}{*}{DT} & Histogram peaks in WT & BPSK, QPSK, SPSK, & & & & & & \\  & & magnitude and mean & 16 PSK, 2 QAM & & & & & & & \\  & & and variance of & & 4 QAM, 8 QAM, 16 QAM, 16 QAM, 16 PSK & &