Spectrum Sensing Using Fast Slepian Transform for Cognitive Radio Networks

Efficient and reliable spectrum sensing is essential for cognitive radio networks, especially in low signal-to-noise ratio (SNR) environments. Traditional methods such as energy detection often suffer from high false alarm rates and reduced detection accuracy under noise. This paper presents a novel spectrum sensing framework based on the Fast Slepian Transform (FST), which leverages the time-frequency concentration properties of Discrete Prolate Spheroidal Sequences (DPSS). By projecting noisy RF signals onto the DPSS basis, the FST yields a sparse spectral representation that enables robust detection of occupied channels.We evaluate both fixed and adaptive thresholding techniques, with the latter optimized using the F1 score to balance detection and false alarm rates. Simulation results demonstrate significant improvements in detection probability and reduction in false alarms compared to traditional methods, confirming the effectiveness of the proposed FST-based sensing approach for low-SNR cognitive radio environments.