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Resource Allocation in Multi-Cluster Quantum Cognitive Radio Network (QCRN) With GAN-Based Spectrum Prediction
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Author(s): M. Ponnrajakumari (Velammal Engineering College, India), G. Subramanian (Velammal Engineering College, India), S. Porselvan (Velammal Engineering College, India)and T. Shabareesh (Velammal Engineering College, India)
Copyright: 2025
Pages: 14
Source title:
Multidisciplinary Applications of AI and Quantum Networking
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)and Jose Anand (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-9336-9.ch003
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Abstract
Cognitive radio technology has emerged as a revolutionary way to meet the ever-increasing demand for wireless spectrum. Quantum cognitive radio networks (QCRN) is an advanced form of cognitive radio network that uses the principles of quantum communication and quantum computing. In cognitive cluster networks, multiple cognitive radios cooperate to optimize spectrum utilization and improve network performance. The proposed GAN-based spectrum prediction framework leverages the power of deep learning to learn and predict spectrum availability in a dynamic and data-driven manner. The clustering method increases the prediction accuracy and reduces the computational complexity, making it ideally suited for real-time spectrum planning in dynamic and heterogeneous wireless environments. It did so in various ways. The findings show that the GAN-based method outperforms the traditional methods, providing improved estimation
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