# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### DL-Based MC
In [93], the authors design and implement lightweight CNN (LCNN) based MC methods, i.e., the ShuffleMC method for the IoT cyber-physical systems. The ShuffleMC technique requires considerably fewer parameters and is far less computationally complex than the CNN-based MC method but the classification performance of both is almost the same at high SNR. Furthermore, the authors introduce the FFT to pre-process the received OFDM signals for improved classification performance and training acceleration. In addition, \(l_{2}\) regularization is used in the training procedure to minimize over-fitting and marginally enhance classification performance. In [94], a hierarchical CNN-based MC is developed for the waveform and MC in radar communications systems. Using time-frequency representation of the received signal from the Fourier synchrosqueezing transformation (FSST) and deep CNN, the received signal is categorized as either SC radar signals or multicarrier radar signals. Then the cyclic prefix duration, the number of subcarriers, and subcarriers spacing are estimated for the received OFDM signal. After that, the independent component analysis (ICA) operation is used to make the I- and Q-components, which are fed into the CNN classifier for MC. In [95], a spectrum interference-based two-level data augmentation method in CNN for MC is studied. The short-time Fourier transform (STFT) and IFFT are used to assist in the expansion of signals and the introduction of variations while maintaining the key characteristics. The frequency-domain data are provided to radio signals to improve modulation classification. Experimental results demonstrate that using a two-level data augmentation approach based on spectrum interference may considerably enhance the accuracy of the deep CNN for MC, especially when the SNR is low. This methodology obtains state-of-the-art classification accuracy when compared to a range of data augmentation approaches and leading modulation classification algorithms using the public dataset RadioML 2016.10a. In [97], a CNN-based MC algorithm is designed, which used a novel data generation technique allowing deep networks to compute correlations between samples inside each OFDM symbol and between symbols. The authors construct a unique advanced processing block that integrates attention and residual connections to boost the learning efficiency of the model. This approach is tested on a synthetic OFDM signal dataset and shows improved classification performance under various channel circumstances. The cross-talk between sub-carrier has been addressed in terms of CFO. The errors in CFO destroys the orthogonality among the subcarriers or subchannels, thereby introducing ICI. Therefore, classification performance for the MC algorithm may degrade due to ICI or the presence of CFO. Therefore, we need to estimate and compensate for the CFO before MC [63]. In the paper [62], the amplitude moments and correlation properties are used to classify the modulation scheme for OFDM systems. This technique considered the presence of CFO, which is the cause of ICI in the amplitude moments of the received signal. Therefore, the ICI component is estimated and eliminated by using the correlation between the subcarriers. This approach achieves the desired classification accuracy at 30 dB SNR for the normalized CFO for range \(0.1\leq\epsilon\leq 0.2\). In [63], the authors use the DFT and fourth-order cumulant to classify the modulation scheme in the presence of CFO. However, this technique has good classification accuracy for the normalized carrier frequency offset of range \(-0.5\leq\epsilon\leq 0.5\). In [90], a CNN-based MC is studied to classify modulation format for OFDM systems in the presence of CFO. FFT window banks (FWB) are utilizedas 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.