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.

中文翻译：

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传统和先进的自动调制分类技术、挑战和一些新趋势的调查

自动调制分类（AMC）是智能无线通信接收机的一个重要阶段。它是信号检测之后、解调之前的一个必要过程。它在各种应用中起着至关重要的作用。盲调制分类是一项非常困难的任务，如果没有关于发射信号和接收机参数（如载波频率、信号功率、定时信息、相位偏移、频率选择性多径衰落的存在以及实际应用中的时变信道）的信息。AMC方法分为传统方法和高级方法。传统方法包括基于似然（LB）和基于特征（FB）的方法。高级方法包括深度学习 (DL) 方法。此外，AMC 方法用于对不同的调制方案进行分类，例如 ASK、PSK、FSK、PAM、和具有不同阶数和不同信噪比 (SNR) 的 QAM。本文重点总结了 AMC 方法，比较了它们，介绍了 AMC 的商业软件包，最后考虑了 AMC 实施中的新挑战。