Deep learning (DL) has recently become a key technology supporting radio frequency (RF)
signal classification applications. Given the heavy DL training requirement, adopting …

Opening up of spectrum for shared use, such as the Citizen Radio Broadband Service
(CBRS) band, offers unprecedented opportunities for allowing commercial operators to …

Collecting an over-the-air wireless communications training dataset for deep learning-based
communication tasks is relatively simple. However, labeling the dataset requires expert …

Training on time-series data generated from mobile networks is a resource-intensive and
time-consuming task that encounters various training failures. To cope with this issue, we …

Automated spectrum monitoring necessitates the accurate detection of low probability of
intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in …

The task of classifying radio frequency (RF) signals has seen recent success in employing
deep neural network models. In this work, we present a combined signal processing and …

Effective recognition of communication jammings is essential to maintain the integrity of the
electromagnetic spectrum space. In this paper, we propose a novel open-set jamming …

Algorithmic approaches to learning problems are essentially about discerning order within
seemingly random datasets. The theoretical exploration of such algorithms seeks to …

This study presents an enhancement to the Direct Sequence Spread Spectrum (DSSS)
algorithm aimed at transmitting high data rate signals over low-frequency passbands. By …