Machine Learning-Based Cyclostationary Spectrum Sensing in Cognitive Dual Satellite Networks.

Efficient and reliable utilization of the electro- magnetic spectrum remains a significant challenge in wireless and satellite communication. To address this, cognitive satellite networks rely on spectrum sensing, a vital aspect that enables the detection and efficient usage of available spectrum. Classic spectrum sensing methods have been developed, with cyclostationary feature detection (CFD) techniques proving robust against noise. However, the adaptation of CFD techniques to cognitive satellite networks is still in its early stages. This paper introduces a machine learning-based cyclostationary spectrum sensing approach for cognitive dual satellite networks, harnessing machine learning to enhance traditional CFD methods in satellite environments. Simulation results demonstrate that the proposed approach surpasses the conventional cyclostationary spectrum sensing method across various signal-to-noise ratio conditions.