Automatic modulation recognition (AMR) detects the modulation scheme of the received
signals for further signal processing without needing prior information, and provides the …

The linear inverse problem is fundamental to the development of various scientific areas.
Innumerable attempts have been carried out to solve different variants of the linear inverse …

In the past ten years, many high-quality datasets have been released to support the rapid
development of deep learning in the fields of computer vision, voice, and natural language …

Modulation classification is one of the key tasks for communications systems monitoring,
management, and control for addressing technical issues, including spectrum awareness …

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 …

Deep learning (DL) is a new machine learning (ML) methodology that has found successful
implementations in many application domains. However, its usage in communications …

Intelligent fault diagnosis techniques play an important role in improving the abilities of
automated monitoring, inference, and decision making for the repair and maintenance of …

Deep learning (DL) models are vulnerable to adversarial attacks, by adding a subtle
perturbation which is imperceptible to the human eye, a convolutional neural network (CNN) …

Automatic modulation classification (AMC) is an essential and indispensable topic in the
development of cognitive radios. It is the cornerstone of adaptive modulation and …

Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in
future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly …