A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Summary Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency‐selective multipath fading, and time‐varying channels in real‐world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood‐based (LB) and feature‐based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal‐to‐noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC.