Model order selection and meta analysis-based cooperative wideband spectrum sensing

This letter introduces two innovative solutions for cooperative wideband spectrum sensing (WSS) that obviate the requirement for prior knowledge of noise power at the sensors and primary users (PUs) signals. The first method employs an information theoretic criteria (ITC)-based approach, presenting a threshold-free solution. The second method harnesses sensor cooperation through a novel mixture detector based on meta-analysis, a statistical method that combines results from multiple independent tests. To evaluate the efficacy of the proposed detectors, we conduct a comprehensive case study that considers shadowing effects and frequency-selective multipath channels between PUs and sensors. Our results demonstrate that the two WSS methods exhibit remarkable detection performance, particularly in low signal-to-noise ratio (SNR) regimes, outperforming a set of machine learning-based state-of-the-art solutions.