Modulation recognition is an important research branch in the field of communication, which
is widely used in civil and military fields. The classic methods depend on decision theory …

In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable
interest in the field of time series analysis, due to their ability to capture dependencies …

Doppler through-wall radar (TWR) is a promising device for the Internet of Things (IoT),
effective for indoor tracking, health monitoring, and smart homes. However, employing it to …

In recent years, neural network algorithms have demonstrated tremendous potential for
modulation classification. Deep learning methods typically take raw signals or convert …

Deep learning has been widely used in automatic modulation classification (AMC) recently.
Most of deep learning-based AMC uses a single network model to deal with radio signals …

In recent years, data-driven deep learning methods have significantly improved the
performance of automatic modulation classification (AMC). However, labeling the vast …

Deep neural networks provide intelligent solutions for Automatic Modulation Classification
(AMC) tasks in the field of communication. However, their susceptibility to adversarial …

Modern power grids are faced with a series of challenges, such as the ever-increasing
demand for renewable energy sources, extensive urbanization, climate and energy crisis …

Contextual information has been widely used to improve results of semantic segmentation.
However, most approaches investigate contextual dependencies through self-attention and …

Deep learning (DL)-based automatic modulation classification (AMC) has made
breakthroughs and is generally used for signal detection and recognition in wireless …