# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### DL-Based MC  
A lot of focus has recently been drawn by DL due to its effective ability to integrate offline preparation and online deployment [115]. DL is a specialist in automated feature extraction from a huge amount of data instead of the complicated and challenging nature of man-made features [116; 117]. In the paper [82], a cepstral algorithm for MC is proposed with adaptive modulation in OFDM systems. The expert domain features of the received signal are extracted using Mel frequency cepstral coefficients (MFCCs), and the modulation formats and their order are classified using a multi-layer feed-forward neural network. This classifier has the capability of recognizing the M-ary amplitude-shift keying (M-ASK), MSK, M-PSK, M-ary frequency-shift-keying (M-FSK), M-QAM signals and the order of the identified modulation. The classification performance of the proposed technique is evaluated using the false classification probability (FCP). The AWGN channel is taken into account when creating the mathematical model for most of the results. The simulation results reveal that the modulation format and order can identify by extracting cepstral features from the received signal and with the help of the transforms, such as discrete cosine transform (DCT), discrete sine transform (DST), and the discrete wavelet transform (DWT). These classify the distinct features using a robust back-propagation feed-forward neural network for different modulations, such as QPSK, 8-QAM, 16-QAM, 32-QAM, 64-QAM, and 128-QAM. The proposition to identify the modulation type and order is proven to be considered effective. In the paper [83], the authors develop an MC algorithm that is based on the bispectrum and CNN AlexNet models. As we know, bispectrum is a high-order statistic that suppresses AWGN well and is frequently utilized in signal detection and nonlinear system characterization areas. Furthermore, AlexNet exhibits outstanding image classification performance despite having a very basic structure of eight layers. First, the authors compute the bispectrum of received signals, then take the amplitude spectrum of the bispectrum (ASB), which is used as input to the CNN network. After that, they fine-tune the chosen AlexNet, which automatically extracts the distinct features from ASB images. Finally, these features are passed into a softmax classifier, which classifies the modulation type. The simulations are performed under different noise environments for the dataset that includes BPSK, 2-ASK, 2-FSK, 4-FSK, 8-FSK, linear frequency modulation (LFM), and OFDM signals. It is observed that the bispectrum-AlexNet model has a classification accuracy greater than 97.7% when the SNR is greater than 5 dB. The above MC techniques are developed by utilizing feature extraction-based machine learning. Moreover, the standard approaches face bottlenecks, where the PCC is very small and it is also impossible to incorporate them in realistic OFDM systems because it is difficult to extract distinct features from OFDM signals using conventional methods. In order to address this problem, the authors [84] suggest a CNN-based MC system for recognizing OFDM signals. In particular, a CNN is used to train in-phase (I) and quadrature (Q) samples for OFDM signals. The authors construct two datasets with separate modulations for the MC function. Dataset 1 contains BPSK, QPSK, 8-PSK, and 16-QAM modulations, while dataset 2 contains BPSK, QPSK, 8-PSK, 16-QAM, and 64-QAM modulations. These two datasets are utilized to test the robustness of the proposed system. Each modulation format considers 20,000 data samples for training and research. Since CNN can efficiently extract the distinct features of received OFDM signals, the simulation results indicate that CNN trained on I and Q samples achieves better classification performance than conventional machine learning based approaches. In the paper [85], a CNN-based MC method by considering the phase offset effect is studied.