Automatic modulation classification (AMC) is becoming increasingly important in spectrum
monitoring and cognitive radio. However, most existing modulation classification algorithms …

The demand for bandwidth-critical applications has stimulated the research community not
only to develop new ways of communication, but also to use the existing spectrum efficiently …

Automatic modulation classification (AMC) can generally be divided into knowledge-based
methods and data-driven methods. In this paper, we explore combining the knowledge …

Automated Modulation Classification (AMC) has been applied in various emerging areas
such as cognitive radio (CR). In our paper, we propose a deep learning-based AMC method …

Automatic modulation classification (AMC) plays a key role in non-cooperative
communication systems. Feature-based (FB) methods have been widely studied in …

Signal modulation recognition is the core of cognitive radio and spectrum sensing. With the
rapid development and application of deep learning technology in recent years, multi-modal …

In this paper, we present a comprehensive survey and detailed comparison of techniques
that have been applied to the problem of identifying the type of modulation contained within …

Modulation recognition becomes unreliable at low signal-to-noise ratio (SNR) over fading
channel. A novel method is proposed to recognize the digital modulated signals with …

With the rapid development of information nowadays, spectrum resources are becoming
more and more scarce, leading to a shift in the research direction from the modulation …

Automatic Modulation Classification (AMC) is crucial for ensuring secure and efficient
operation of the Internet of Things (IoT). In this study, we focus on addressing the challenge …