# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### ML-Based MC  
In this paper [73], the authors extract the features by calculating higher-order cumulants, then the extracted features are applied to naive Bayes classifier for MC. However, the authors assume proper equalized and perfectly synchronized signals received at the receiver. The features, combinations of fourth-order \(C_{42}\) and sixth-order cumulants \(C_{63}\) often produce better classification performance than using each of these features alone. By using the same set of features, the naive Bayes classifier is compared with the ML-based classifier and SVM-based classifier. It is observed that the naive Bayes classifier outperforms the ML-based classifier and SVM-based classifier with less computational complexity. This paper [74] introduces a technique for classifying OFDM signals using higher-order moments and cumulants with multiple types of classifiers and cluster techniques. There are four considered methods of classification, namely, KNN, ML, SVM, and neural network (NN) classifiers. Fuzzy \(k\)-Means and fuzzy \(c\)-means are two cluster techniques that are used for the two classes of OFDM signals. One class is considered fixed WiMAX (IEEE 802.16d), which includes BPSK, QPSK, 16-QAM, and 64-QAM modulations. Another class is considered OFDM signals used in Wi-Fi (IEEE 802.11a), which includes and BPSK, QPSK, 16-QAM, and 64-QAM modulations. In the simulations, the input signals are normalized to have zero mean and unit variance after transmitting through the Rayleigh fading channel. The normalized output signal is then used for the feature extraction process. Higher-order moments and cumulants up to the 8 th order are used to extract features. The extracted features are used as input to the different types of classifiers, such as SVM, KNN, ML, and NN classifiers, which use the fuzzy \(k\)-means and fuzzy \(c\)-means as clustering techniques. The performance of the SVM classifier with the fuzzy \(k\)-mean is better than all the combinations of classifiers and clustering algorithms for most of the SNR values. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author(s)** & **Classifier(s)** & **Modulation(s)** & **Parameter(s)** & **Channel(s)** & **Average PCC at 20 dB SNR** \\ \hline \multirow{3}{*}{[PERSON] [73]} & Optimize Shannon’s channel capacity, Naive Bayes classifier & BPSK, QPSK, 16-QAM and 64-QAM & Perfect synchronization & AWGN & 96.8\% \\ \hline \multirow{3}{*}{[PERSON] [74]} & Higher order moments and cumulants, Fuzzy \(K\)-Means and Fuzzy C-means & BPSK, QPSK, 16 QAM, and 64 QAM & - & Rayleigh & 100\% \\ \hline \multirow{3}{*}{[PERSON] [75]} & Higher-order cumulants, random forest based MC algorithm & QPSK, 16-QAM and 64-QAM & Imperfect time synchronization & Frequency-selective & 100\% \\ \hline \multirow{3}{*}{[PERSON] [76]} & Least squares (LS) method and iterative closest point (ICP) & BPSK, QPSK, 16-QAM, and 64-QAM & Unknown noise variance and CSI & Flat fading & 97.5\% \\ \hline \multirow{3}{*}{[PERSON] [77]} & High order cumulants, Decision Tree classifier & BPSK, QPSK, 16-QAM, 64-QAM and OFDM & Presence of timing offset & Flat fading & 99.5\% \\ \hline \multirow{3}{*}{[PERSON] [78]} & Higher order statistics, pattern recognition methods, ANN or SVM, or RFC or KNN & QPSK and 16-QAM and & Presence of CFO and Imperfect CSI & Rayleigh & 100\% \\ \hline \multirow{3}{*}{[PERSON] [79]} & Peaks in the distribution of amplitude, the variance of the amplitude, the variance of the phase, and the variance of the spectrum, SVM classifier & BPSK, QPSK, 16-QAM, 64-QAM, 256-QAM and GMSK & Unknown CFO & AWGN & 100\% \\ \hline \multirow{3}{*}{[PERSON] [80]} & Clustering and Gaussian model & QPSK, 16-QAM, 64-QAM & - & AWGN & 100\% \\ \hline \multirow{3}{*}{[PERSON]. [PERSON] [81]} & High order cumulants and bi-spectral envelope peaks, hierarchical iterative SVM classifier model & M-QAM, MFSK and MPSK & Perfect synchronization & Rayleigh & 100\% \\ \hline \hline \end{tabular}
\end{table}