Resource Allocation in Multi-Cluster Quantum Cognitive Radio Network (QCRN) With GAN-Based Spectrum Prediction
M Ponnrajakumari, G. Subramanian, S. Porselvan, T. Shabareesh · Advances in computational intelligence and robotics book series · 2024
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