# A Survey of Blind Modulation Classification Techniques for OFDM Signals
###### Abstract  
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed. blind modulation classification; orthogonal frequency division multiplexing; higher-order cumulant and cyclic cumulant; maximum-likelihood; maximum a posteriori; deep learning; convolutional neural networks; probability of correct classification; testbed implementation  
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(FB) [1; 5; 6; 11; 12; 13; 14; 15; 16; 17]. Although the LB approaches are optimal in a Bayesian context, they have high computational complexity [11]. They often necessitate prior information about the signal parameters in order to distinguish modulation formats, which is typically undesirable in an intelligent or adaptive transceiver system. Furthermore, FB algorithms, which consist of features extraction and classifier construction, usually provide a sub-optimal solution. They are inherently simpler to implement, have less computational complexity, and may not necessitate prior information about the signal parameters and channel statistics. To identify the modulation schemes, existing FB approaches extract specific features, such as cumulants [12; 13], cyclic statistics [5; 6; 14; 15; 16], and wavelet transform [17], and use threshold values to distinguish the extracted features. As a result, they are better fit for fading and additive white Gaussian noise (AWGN) channels. The algorithms [5; 6; 14; 15; 16; 18; 19; 20; 21] based on higher-order cyclic statistics are reliable and perform well in flat fading as well as in frequency-selective fading channels. They consider M-ary phase-shift keying (M-PSK) and M-ary quadrature amplitude modulation (M-QAM) modulation schemes by using non-zero cyclic frequencies of received signals. The combination of higher-order correlation-, cumulant-, cyclic cumulant-, and cyclostationarity-based MC algorithm for multiple-antenna systems is analyzed in [5]. The algorithm described in [6] is designed for single-antenna and single-carrier (SC) systems. It requires the combined features of cumulants and cyclic cumulants and performs well over flat fading channels. Furthermore, the algorithm proposed in [5; 6] can also distinguish various quadrature PSK (QPSK) variants, such as offset QPSK (OQPSK), minimum-shift keying (MSK), and \(\pi\)/4-QPSK modulation types. Recent advances in machine learning (ML) and data science have resulted in its extensive application in various fields.