# A Survey of Blind Modulation Classification Techniques for OFDM Signals
###### Abstract
Artificial intelligence (AI) and other advanced ML approaches have significantly improved state-of-the-art outcomes in computer vision, speech recognition [22], drug discovery, genomics, and, most recently, physical layer communication [23]. MC algorithms [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36] focused on various ML algorithms. In [24], the MC technique is evaluated using genetic programming (GP) and K-nearest neighbor (KNN). Cumulants are utilized by GP as input features to distinguish modulation types. In [25], extreme learning machine (ELM) and higher-order statistics-based MC algorithms for multiple antenna systems are presented. Convolutional neural networks (CNNs) are explored in [26] that can distinguish modulation schemes even at low signal-to-noise ratio (SNR) scenarios. Furthermore, CNN-based MC techniques are robust to prediction errors on carrier phase offset and SNR. The approach investigated in [27] extracts unique characteristics using higher-order cumulants (HOCs), and then the feed-forward neural network model is developed to distinguish modulation schemes. In comparison to typical centralized training, distributed learning-based MC (DistMC) based on several edge devices can achieve a faster training process and reduce communication costs through collaborative training [28]. Multi-task learning (MTL) based MC has a single trained model for all SNRs under carrier frequency offset (CFO) and phase offset (PO) conditions [29; 30]. In [35], an adversarial transfer learning-based MC developed a framework for SC systems that combines transfer learning with adversarial networks to handle the problem of limited data in a realistic scenario. A complex-valued network [36] is presented to illustrate the enormous potential for MC and show the higher classification performance as compared to the real-valued network. The authors [37] studied a phoneme-based distribution regularization algorithm for speech enhancement by utilizing speech recognition information in the modulation domain. However, the approaches mentioned above [1; 5; 6; 11; 12; 13; 14; 15; 16; 17] are only applicable to SC systems. Orthogonal frequency division multiplexing (OFDM) is a well-known multicarrier modulation technology used in advanced wireless communications systems. OFDM is employed in the 4G Third-Generation Partnership Project (3 GPP) Long Term Evolution-Advanced (LTE/LTE-A), Worldwide Interoperability for Microwave Access (WiMAX), and high-speed wireless local area network (WLAN) standards such as 802.11n [19]. It is also an integral part of 5G New Radio (NR) cellular. The key feature of OFDM is the ability to convert frequency-selective fading to flat fading channels. Due to the high spectrum utilization and strong anti-multipath interference ability, the OFDM modulation scheme has been employed as the main transmission approach for high data rate systems [38; 39]. M-PSK and M-QAM are the two most popular modulation schemes that are used with OFDM. MC for OFDM signals is a critical research challenge for 5G and beyond wireless communication, where AI would be a fundamental aspect of the communication system [40; 41; 42; 43]. Various MC algorithms for the OFDM systems were carried out in [44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54;