# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### DL-Based MC
As shown in Figure 6, CNN-based MC is first trained by the received I and Q samples in the presence of phase offsets at different values of SNR. As shown in Figure 7, the DL-based MC technique is mainly implemented by CNN. The PReLU is used as an activation function for all layers, except the last layer, and the softmax is used as an activation for the last layer to implement a multi-classification problem. The authors use two sets of data with different modulation modes for the MC issue, i.e., Dataset 1 and Dataset 2, to verify the robustness of the classification technique [85]. The number of subcarrier \(N=16\), CP length \(N_{cp}=2\), number of OFDM symbol 6 and AWGN channel are considered the simulation parameters for this technique. Comparative experiments show that its performance of classification is much higher than the conventional extraction methods. Moreover, the classification accuracy relatively reduces at the low SNRs due to the presence of phase offsets. By gradually increasing the SNRs, effective classification accuracy can be achieved eventually. An adaptive modulation model based on machine learning for a MIMO-OFDM system is carried out in [44]. The 5G new radio (NR) technology can be used in a wider range of internet of things (IoT) applications than traditional systems. The adaptive modulation technique, which changes data rate and latency based on channel conditions, can be efficiently employed in 5G digital NR technology. The traditional adaptive modulation technique is developed by assigning modulation formats based on the channel conditions since the rule-based MC is unable to analyze transmission efficiency based on channel correlations between antennas. The number of propagation modes is enhanced exponentially based on the available number of modulations and antennas. So, these are not appropriate for 5G NR systems. The proposed adaptive modulation technique is learned by the training data, which are generated by the feature extracted from the received signal. The DNN application for adaptive modulation is the primary method of the main component analysis, which improves the model efficiency. The simulation results on the optimal transmission mode classification for the MIMO-OFDM signal show that the proposed model supports adaptability according to the condition of the complex MIMO channel. In [86], the authors here present a CNN-based MC approach for the identification of OFDM signals, which is linked to a CNN that is trained on I and Q samples. The suggested CNN-MC technique is made up of two parts: three convolutional layers and four fully  
Figure 6: Framework of the proposed CNN-based MC system [85]. Figure 7: CNN structure design in the proposed CNN-based MC method [85]. connected layers. The number of subcarrier \(N=16\), CP length \(N_{cp}=2\), number of OFDM symbol 6 and Rician channel are considered the simulation parameters for this technique. The suggested technique outperforms existing modulation classification algorithms in terms of accuracy and reliability. However, any parameters, such as number of subcarriers, number of null subcarriers, STO, CFO, phase offset, and CP length, change these MC and do not provide accuracy of more than 50% for adaptive OFDM systems. The other approach described in [87] focuses on two-step training to enhance the classification performance of CNN-based classifiers. Transfer learning is also introduced to increase the performance of the retraining. A wider range of modulation formats for the OFDM signal, such as BPSK, QPSK 8-PSK, 16-QAM, 32-QAM, and 64-QAM, is recognized by the suggested technique. In [88], the MC algorithm for the OFDM signal is developed by using an intelligent pyramid model. This algorithm has four stages, i.e., pre-processing, feature extraction, feature clustering, and classification. In the pre-processing step, the authors improve the received signal quality, which involves two steps quality evaluation and quality augmentation, using the bi-fold signal fortification (BFSF) approach.