# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### ML-Based MC
Multiple signal classification (MUSIC) algorithms are used to estimate the CFO and channel statistics. The estimated CFO and channels are compensated and equalized, then features are extracted using higher-order moments (HOMs) and HOCs. Finally, the extracted features are applied to ANN, SVM, RF classifier (RFC), and KNN classifier. The simulation results show that the SVM and ANN classifiers have better classification performance, even at low SNR. In [79], an SVM-based MC algorithm is studied for the OFDM system in the presence of frequency offset in which statistics-based features are used as input of the SVM classifier. The number of peaks in the distribution of amplitude, the variance of the amplitude, the variance of the phase, and the variance of the spectrum are extracted from the received signal. These extracted features are used to make a dataset. This dataset is applied to the SVM classifier to classify the modulation scheme of the received signal. The proposed method shows great accuracy in high SNR channels with over 80% accuracy. It also shows robustness against the frequency offset. However, when the signal is flooded by noise and extremely influenced by frequency offset, the proposed still has over 50% accuracy. The algorithm is tested experimentally on the SDR platform, which can realize a variety of communication systems by updating the software. Based on such a popular SDR hardware platform and using GNU Radio, the modulation formats are generated, transmitted, and classified. In [80], the design and implementation of the MC algorithm for the OFDM visible light communication (OFDM-VLC) system are explored. Clustering and Gaussian model analysis are used to obtain the classification feature values. The modulation format is then classified using these classification feature values. The simulation results show that the suggested method can achieve 100% classification accuracy at 1 dB to 2 dB lower than that of the clustering scheme. Furthermore, the experimental findings show that the suggested MC technique is feasible in an OFDM-VLC system. In [81], an MC algorithm base on a hierarchical iterative SVM classifier is studied for the OFDM signal. To extract characteristic values from OFDM signals, higher-order cumulants and bi-spectral envelope peaks are used, and the resulting characteristic values are then processed to create fresh training sample data. The feature extracted by using HOCs is used to distinguish multicarrier signals from the SC signals. The bi-spectral envelope peaks are used to distinguish the OFDM signal from the multicarrier signal. The training dataset obtained from the higher-order cumulants and bi-spectral envelope peaks of the received signal is applied to the input of a hierarchical iterative SVM classifier. The number of subcarrier \(N=128\), CP length \(N_{cp}=N/4\), symbol rate 1024 bps, sampling rate 3000 kHz and Rayleigh channel are considered the simulation parameters for this technique. It is found the classification accuracy of the SVM-based classifier is improved when compared with the wavelet transform method and higher-order cumulant-based method.