Next-Gen solutions: Deep learning-enhanced design of joint cognitive radar and communication systems for noisy channel environments

Abstract

In recent years, the dual-function radar and communication (DFRC) paradigm has emerged as a focal point in addressing spectrum congestion challenges. However, prevailing research heavily relies on computationally complex likelihood-based approaches for communication signals with an added Gaussian noise based single waveform. Note that, a single waveform for diverse scenarios e.g., presence of a communication receiver in the radar main lobe, side lobe, etc., may lead to a deteriorated detection performance in a DFRC design. Therefore, in this paper, we present a cognitive DFRC architecture that utilizes a diverse set of orthogonal waveforms at the transmitter. Specifically, based on a perception-action cycle, a QAM-based waveform is employed for communication when both the radar target and communication receiver are within the main lobe, while a PSK-based waveform is used when the radar target is in the main lobe and the communication receiver is in the side lobes. Furthermore, to enhance the feature-based estimation, the communication receiver integrates a Convolutional Neural Network (CNN) architecture designed to autonomously learn and extract features from received signals with different Signal-to-Noise ratio (SNR). Next, the adaptive nature of the system enables proficient discernment of the received signal type and its corresponding SNR value. Moreover, deep learning techniques are applied in realistic scenarios with various channel impairments to extract features from received signals, departing significantly from likelihood-based methods and reducing computational complexity. The proposed methodology’s effectiveness is validated through Monte Carlo simulations, underscoring its potential to address challenges associated with DFRC under real-world conditions.