# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 3 Statistics-Based Approach to MC
### FB Approach
The result is well separated between OFDM and SC modulations. When SNR is 0 dB, the PCC between OFDM and SC signals can reach 100% when the symbol rate is greater than 20 kHz. Another FB-MC [65] is using spectrum analysis to classify the OFDM and SC. The authors utilize the energy distribution parameter and the kurtosis of the power spectrum coefficient to classify OFDM and SC. This method does not need any prior information about the symbol rate, carrier frequency, etc. In simulation results, it is found that extracted spectrum parameters have better performance over AWGN as well as Rayleigh fading channels. It has a classification rate of up to 97% with an SNR at 10 dB. In [66], the peak-density clustering algorithm is used to investigate an MC technique for adaptive optical OFDM systems. The clustering technique is used to find the centers  
Figure 4: Histogram of \(\mathcal{C}_{42_{R}}\) for BPSK, QPSK, QQPSK, MSK, and 16-QAM. Adapted with permission from Ref. [63]. Copyright 2021 IEEE. Figure 5: Histogram of \(\mathcal{C}_{42_{R}}\) for BPSK and QPSK. Adapted with permission from Ref. [63]. Copyright 2021 IEEE. of the signal constellation clusters. The number of cluster centers is calculated using the density and distance metrics of samples. The number of cluster centers is utilized to distinguish M-QAM. The OFDM signals are fed into an arbitrary waveform generator (AWG) with a sampling rate of 50 GSamples/s. The electrical OFDM signal is then converted into an optical signal using an external cavity laser (ECL) and an intensity modulator. The modulated optical signal is then routed through a variable optical attenuator (VOA) and an erbium-doped fiber amplifier (EDFA) to alter the signal SNR and mimic different transmission circumstances. They use a 50 GSamples/s real-time oscilloscope to capture data and another VOA to regulate the input power before the photodetector. Finally, OFDM MC and demodulation are conducted for a test sample of 8192 lengths in each optical SNR. In the paper [67], the identification of these orthogonal modulations, i.e., OFDM, code division multiple access (CDMA), is studied. The classification method is based on general orthogonal modulations, whose modulation parameters should be estimated. The identification method applies to both adaptive modulation and increased security in radio communications. General orthogonal modulations are employed to identify modulations. First, the modulation parameters of rotation planes and angles are estimated. Orthonormal vectors are derived by received signal samples and rotated to hold orthogonality among time slots. Then, the inverse rotation corresponds to the modulation parameters to be estimated. The difference vector between the received signal vectors is used for this method. In computer simulations, OFDM, CDMA, a block of QAM, and so on are considered candidate modulations. The bit error probability of the estimated modulation is presented to compare the performance from the point of view of SNR and the number of samples. The proposed estimation performance is evaluated in the AWGN channel by computer simulations. Based on the higher-order cumulants, an MC algorithm is carried out that discriminates the OFDM signals from SC signals [68]. First, OFDM signals are discriminated from SC signals based on distinct features parameters over the Rayleigh channel. In order to verify the effectiveness, the modulation set is assumed as OFDM, 2-FSK, 4-FSK, 8-FSK, BPSK, QPSK, 8-PSK, 16-QAM, 32-QAM, and 64-QAM. The combination of the second- and fourth-order cumulants is used as the feature to discriminate the OFDM signals from the SC signals. Simulation results show that the algorithm is stable with low computational complexity and high PCC in low SNR level. In the paper [69], a classification technique is devised for identifying the OFDM signals from the SC. In addition to differentiating the OFDM signals from SC, some important parameters of OFDM signals are estimated for further processing.