# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC
### ML-Based MC
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Classifier(s)** & **Modulation(s)** & **Parameter(s)** & **Channel(s)** & **Average PCC at 20 dB SNR \\ \hline \multirow{4}{*}{[PERSON] [82]} & Mel Frequency Cepstral Coefficients (MFCCs) & QPSK, 8-QAM, & Perfect synchronization & AWGN & 100\% \\  & and multi-layer feed & 64-QAM and 128-QAM & & & \\  & -forward neural network & & & & \\ \hline \multirow{4}{*}{[PERSON] [83]} & Bispectrum and CNN Alexnet model & BPSK, 2-PSK, & & & \\  & & LFM, and OFDM & & & \\ \hline \multirow{2}{*}{[PERSON]. [PERSON] [84]} & CNN with dropout layer & BPSK, QPSK, 8-PSK, & Perfect synchronization & Rician fading & 99\% \\  & & 16-QAM and 64-QAM & & & \\ \hline \multirow{2}{*}{[PERSON] [85]} & CNN, ReLU and PRelu activation & BPSK, QPSK, and 16-QAM & Presence of phase offset and imperfect CSI & AWGN & 100\% \\ \cline{1-1} \cline{2-6}  & & BPSK, QPSK, 4-PAM, & Perfect synchronization & Rician fading & 97.5\% \\ \cline{1-1} \cline{2-6}  & & 8-PSK, and 16-QAM & & & \\ \hline \multirow{2}{*}{[PERSON] [87]} & CNN with two step training, Transfer learning & BPSK, QPSK, 8-PSK, & Unknown CFO and unknown SNR & Time invariant and frequency non-selective & 100\% \\ \cline{1-1} \cline{2-6}  & & QPSK, BPSK, DPSK, & & & \\ \cline{1-1} \cline{2-6}  & GLP-Net, TF-MDS, MDNC, and IQL & AS,PSK, 16-QAM, & & & \\ \cline{1-1} \cline{2-6}  & & 32-QAM, 64-QAM, & Unknown CFO and AWGN & 86\% \\ \cline{1-1} \cline{2-6}  & & BPSK, QPSK, 8-PSK, & & & \\ \cline{1-1} \cline{2-6}  & & AM-DSB, AM-SSB, & & & \\ \cline{1-1} \cline{2-6}  & & CPFSK, QPSK, WERM, & & & \\ \cline{1-1} \cline{2-6}  & & 4-PAM, 16-QAM, and 64-QAM & & & \\ \hline \multirow{2}{*}{[PERSON] [90]} & IQ and FFT window bank (FWB), CNN-LSTM-based classifier & QPSK, 16-QAM, & & & \\ \cline{1-1} \cline{2-6}  & & Mixed order moment, hybrid grey wolf optimization (HGW) algorithm, DNN-based classifier & QPSK, 16-QAM, & Presence of CFO and STO & Rayleigh & 100\% \\ \hline \multirow{2}{*}{[PERSON] [92]} & Alexnet/CoogLeNet & BPSK, QPSK, 8-QAM, & & & \\  & TL-based classifier & 16-QAM, 32-QAM and 64-QAM & & & \\ \hline \multirow{2}{*}{[PERSON] [93]} & Lightweight CNN (LCNN)-based Shuffle MC, FFT, \(l_{2}\) regularization & BPSK, QPSK, 8-PSK, 16-QAM & & & Rician fading & 100\% \\ \cline{1-1} \cline{2-6}  & & Fourier synchronqueezing transformation (PSST), Independent component analysis (ICA), hierarchical CNN-based MC & & & \\ \hline \multirow{2}{*}{[PERSON] [95]} & Spectrum interference-based & BPSK, QPSK, 8-PSK, 16-QAM, & & & \\  & two-level data augmentation & 64-QAM, GFSK, CPFSK, 4-PAM, & & & \\ \cline{1-1} \cline{2-6}  & method, deep CNN & WERM, AM-SSB, and AM-DSSB & & & \\ \hline \multirow{2}{*}{[PERSON] [97]} & CNN with integrated attention and residual connections & BPSK, QPSK, 8-PSK, and 16-QAM & Presence of CFO & Rayleigh & 88\% \\ \cline{1-1} \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of DL-based classifiers for OFDM signals. that, as compared with the method of using only the least-squares method, the method combining the least squares (LS) method with the iterative closest point (ICP) algorithm does not deteriorate the accuracy of the phase correction, even if the number of signal points decreases. In this paper [77], the classification of OFDM, BPSK, QPSK, Gaussian frequency-shift keying (GFSK), 16-QAM, and 64-QAM is realized by MATLAB programming based on the characteristic of HOCs. A new feature parameter is proposed according to the second- and sixth-order cumulant. Simulations are conducted with classifiers, including KNN, SVM, decision theory, and back-propagation neural network (BPNN). It is found that the average classification rate is greater than 95%. In the paper [78], the authors propose a blind MC algorithm for space-time block coding (STBC)-based MIMO-OFDM system, which works in the presence of CFO, channel estimation errors, and impulsive noise.