This paper presents the application of self-supervised deep contrastive learning in clustering
signals detected in the wideband RF spectrum, presented in the form of spectrograms …

Driven by various academic, standardization and regulatory initiatives, recent research on
spectrum resource utilisation has focused also on technology and transmission classification …

Emerging wireless communication applications have led to a crowded radio frequency (RF)
spectrum. Therefore, it is desired to develop signal separation techniques that can extract …

In recent years, much work has been done on processing of wireless spectrum data
involving machine learning techniques in domain-related problems for cognitive radio …

Intelligent capabilities are of utmost importance in future wireless communication systems.
For optimum resource utilization, wireless communication systems require knowledge of the …

The so-called black-box deep learning (DL) models are increasingly used in classification
tasks across many scientific disciplines, including wireless communications domain. In this …

Current challenges in spectrum monitoring include radar emitter state identification and the
ability to detect changes in radar activity. Recently, large labeled datasets and better …

The analysis of radio frequency (RF) scenes is a critical component of RF situational
awareness and dynamic spectrum management. This work addresses the challenges of RF …

With the development of information technology, radio communication technology has made
rapid progress. Many radio signals that have appeared in space are difficult to classify …

In recent years, the traditional feature engineering process for training machine learning
models is being automated by the feature extraction layers integrated in deep learning …