Predictive Analytics in Cognitive Radio: Neural Network Based Approach for Spectrum Sensing

This work addresses the pressing need for efficient spectrum utilization in the ever-expanding domain of cognitive radio networks. Utilizing a neural network-based approach based on a Multilayer Perceptron (MLP), we develop a channel status predictor that enhances spectrum efficiency without requiring prior knowledge of traffic characteristics. This approach is crucial for green networking as it significantly reduces the energy expended in spectrum sensing and improves operational efficiency. Our MLP model predicts spectrum holes dynamically, allowing for more effective management of the spectrum and reducing energy consumption by avoiding unnecessary sensing. Extensive simulations confirm the model’s capability to enhance spectrum utilization while minimizing prediction errors, demonstrating its potential to contribute to more sustainable and efficient cognitive radio technologies.