Automatic modulation classification is an essential and challenging topic in the development
of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation …

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

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

Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order
to extract relevant information and for making predictions. The performance and the …

COVID‐19 has sparked a worldwide pandemic, with the number of infected cases and
deaths rising on a regular basis. Along with recent advances in soft computing technology …

Deep learning has been widely exploited in radio modulation recognition in recent years. In
this paper, we exploit empirical mode decomposition (EMD) and variational mode …

This paper surveys Artificial Intelligence (AI) methods for acquiring and managing context-of-
operation awareness of radio communication nodes, links, and networks. The meaning and …

A novel method based on constellation structure is proposed to identify PSK and QAM
modulation of different orders, in the slow and flat fading channel. The proposed method …

Fifth Generation (5G) networks must support billions of heterogeneous devices while
guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet …

We propose an unknown radar waveform recognition system for identifying unknown radar
waveforms and classifying known radar waveforms simultaneously, which can be …