# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### DL-Based MC
The number of subcarrier \(N=2048\), CP length \(N_{cp}=3\), sampling frequency 5 MHz and AWGN channel are considered the simulation parameters for this technique. If the received signal quality is poor, then quality augmentation is performed, taking into account noise reduction, equalization, quantization, and CFO compensation. Then the feature extraction process is performed by the gated feature pyramid network (GFP-Net). After that, the authors make the cluster from the extracted feature by using an intelligent twin-functioned human mental search (TF-HMS) optimizer to minimize the classification complexity. Finally, they offer the multi-distance-based nearest centroid classifier (MDNCC) technique, as well as improved Q-learning (IQL), to determine the correct modulation format for the received signal. However, this technique only considers the CFO as the synchronization when performing the modulation classification of the received signal. In [89], a CNN long short-term memory (CNN-LSTM) based dual-stream structure for MC is developed. The first stream extracts local raw temporal characteristics from raw signals, while the second stream learns knowledge from amplitude and phase data. To learn spatial and temporal information from each stream, CNN-LSTM is used, which combines the spatial feature extraction ability of CNN and superior capacity of processing time-series data of LSTM. Furthermore, the features learned from two streams interact in pairs as a result of an effective operation, expanding the diversity of characteristics and, therefore, improving the classification performance of the received signal. In [90], a CNN-based MC is studied in order to classify SC and OFDM systems with varying symbol lengths. The majority of older DL-based MC algorithms misinterpreted OFDM-based signals with varying OFDM usable symbol lengths. To address this issue, FFT window banks (FWB) are utilized as input to the CNN model to estimate the length of an OFDM symbol. After estimating the OFDM symbol length, a CNN-based MC technique is utilized to categorize the OFDM and SC modulation formats concurrently using FWB and IQ samples as combined input. However, compared to the traditional DL-based MC, this technique needed a longer received symbol to obtain the correct classification. In [91], an OFDM signal identification technique based on a hybrid grey wolf optimization (HGWO) algorithm to optimize with a deep neural network model is carried out. This technique can distinguish the OFDM modulation signal from complex signals, such as SC, OFDM signals, and wavelet packet signals (WPM) in a multipath channel. In this technique, mixed order moment \(u_{20}=M_{42}(x_{m})/M_{20}^{2}(x_{m})\), characteristics parameter \(R=\sigma^{2}/\mu^{2}\), and \(N=\frac{BW}{A\gamma}-1\) are extracted from the received signal. Then, a dataset is prepared by using \(u_{20}\), \(R\), and \(N\), which are the input of the classifier. Then the HGWO algorithm is used to optimize the weights and thresholds of the DNN. The experimental findings demonstrate that the suggested method significantly speeds and improves the convergence speed of GWO. When compared to traditional methods, such as particle swarm optimization (PSO) and whale optimization algorithm (WOA), this technique outperformsthe two. However, the HGWO technique in this study is limited because it is only used to improve the weight and threshold of the DNN model, and the network structure must be chosen manually. The intelligent optimization technique applied to the structure of the deep learning network may enhance classification accuracy. In [92], the MC technique for OFDM VLC systems based on transfer learning (TL) is developed. For virtually all SNR values, the suggested AlexNet/GoogLeNet-TL-based strategy outperforms previous approaches in which the AlexNet/GoogLeNet is trained from scratch (AlexNet/GoogLeNet-SC). In more practical, few-training-data circumstances, AlexNet/GoogLeNet-TL outperforms AlexNet/GoogLeNet-SC by a wide margin.