Cellular networks are undergoing a radical transformation toward disaggregated, fully
virtualized, and programmable architectures with increasingly heterogeneous devices and …

In this work, the Transformer Network (TRN) is applied to the automatic modulation
classification (AMC) problem for the first time. Different from the other deep networks, the …

In this paper, we propose a cross-domain, scalable, and interpretable radio frequency (RF)
fingerprinting system using a modified prototypical network (PTN) and an explanation …

Modulation recognition is an important technology in wireless communication systems. In
recent years, deep learning-based modulation recognition algorithms, which can …

Automatic modulation classification (AMC) is gaining greater significance in both military
and civilian contexts. However, the diversity and dynamics of actual wireless communication …

Automatic modulation classification (AMC) has been envisioned as a significant element for
security issues at the physical layer due to its indispensable role in accurate …

Automatic modulation classification plays a significant role in numerous military and civilian
applications. Deep learning methods have attracted increasing attention and achieved …

Deep learning (DL) has been introduced to cognitive radio network to solve the problem of
spectrum scarcity and further enhance the spectrum utilization. However, many DL-based …

Automatic modulation classification for radio signals is an important task in many
applications, including cognitive radio, radio spectrum monitoring and signal decoding in …

Despite the substantial success of deep learning for Automatic Modulation Classification
(AMC), models trained on a specific transmitter configuration and channel model often fail to …