In this paper, we investigate the demodulation performance of radar-embedded
communications, by utilizing deep neural network (DNN) machine learning to extract the …

This paper presents a comprehensive framework for generating radio frequency (RF)
datasets, designing deep learning (DL) detectors, and evaluating their detection …

Due to the increasing demand for spectrum resources, the co-existence of communications
and radar systems has been proposed that allows radar and communications systems to …

in the deep learning field, the availability of datasets is a very important requirement for
developing deep neural network models and benchmarking. This paper introduces a new …

In this work, we investigate the use of the guard bands of a linear frequency modulated
(LFM) radar waveform for communications. For illustration, quaternary phase-shift keying …

In this paper, an efficient deep learning-based waveform recognition method is introduced
for coexistence radar-communication systems in the presence of channel impairments. The …

Recent performance analysis of dual-function radar communications (DFRC) systems, which
embed information using phase shift keying (PSK) into multiple-input multiple-output (MIMO) …

Target detection is one of the most important applications of radar systems. However, the
task of processing echoed signals to determine whether a valid target exists, or if it is just …

The advent of commercial-off-the-shelf (COTS) software-defined radars (SDRs) has enabled
low-cost, flexible experimentation with emerging pulse-agile waveform designs to mitigate …

Detection of radar signals is the initial step for passive systems. Since these systems do not
have prior information about received signal, application of matched filter and general …