Citation

Munir, Muhammad Fahad and Basit, Abdul and Waseem, Athar and Roslee, Mardeni and Khan, Wasim and Umar, Ubaid (2024) Cognitive Fusion of Radar and Communication Functions: Deep Learning Perspectives. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

Text Cognitive Fusion of Radar and Communication Functions_ Deep Learning Perspectives.pdf - Published Version Restricted to Repository staff only Download (525kB)

Abstract

Dual-function radar and communication is an emerging field that involves sharing both hardware, frequency spectrum and signals between radar and communication technologies. This study presents a cognitive architecture tailored for dual-function radar communication (DFRC), that includes five communication schemes and two distinct waveforms. Using deep learning techniques, features are extracted from received signals at the communication receiver. We evaluate the accuracy of this approach across a wide range of signalto-noise ratios (SNR) through simulation. Specifically, we use a Convolutional Neural Network (CNN)-based image classifier to categorize images representing various constellation schemes. Compared to existing techniques, our CNN-based method demonstrates superior classification. Importantly, our algorithm shows resilience against variations in carrier frequency, phase offset, timing errors, and phase jitter

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	06 Feb 2025 07:09
Last Modified:	06 Feb 2025 07:10
URII:	http://shdl.mmu.edu.my/id/eprint/13371

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