Next-gen solutions: deep learning-enhanced design of joint cognitive radar and communication systems for noisy channel environments.

Munir, Muhammad Fahad; Basit, Abdul; Khan, Wasim; Saleem, Ahmed; Khaliq, Aleem; Baig, Nauman Anwar

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.

Citation

MUNIR, M.F., BASIT, A., KHAN, W., SALEEM, A., KHALIQ, A. and BAIG, N.A. 2024. Next-gen solutions: deep learning-enhanced design of joint cognitive radar and communication systems for noisy channel environments. Computers and electrical engineering [online], 120(Part A), article number 109663. Available from: https://doi.org/10.1016/j.compeleceng.2024.109663

• Journal Article Type: Article
• Acceptance Date: Sep 4, 2024
• Online Publication Date: Sep 18, 2024
• Publication Date: Dec 31, 2024
• Deposit Date: Sep 18, 2024
• Publicly Available Date: Sep 18, 2024
• Journal: Computers and electrical engineering
• Print ISSN: 0045-7906
• Electronic ISSN: 1879-0755
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 120
• Issue: Part A
• Article Number: 109663
• DOI: https://doi.org/10.1016/j.compeleceng.2024.109663
• Keywords: Cognitive neural networks; Cognitive architecture; Deep learning; Dual-function radar and communication (DFRC); Channel estimation
• Public URL: https://rgu-repository.worktribe.com/output/2480920