Cooperative Wideband Spectrum Sensing: a Variational Bayesian Inference Approach

The adoption of dynamic spectrum sharing requires addressing various technical challenges, such as intelligent sensing and radio frequency (RF) spectrum awareness. In this scenario, we present a new framework that utilises variational Bayes factor analysis (VBFA) for cooperative wideband spectrum sensing (WSS) without any prior assumption. The approach is data-driven and capable of detecting unused spectrum bands using a test statistic on an evidence lower bound (ELBO). The framework is then applied to a case study that considers shadowing effects and frequency-selective multipath channels between primary users (PUs) and sensors. The solution performs better than the state-of-the-art methods, demonstrating excellent performance in low signal-to-noise ratio (SNR) environments, e.g., reaching a detection probability of 90% for a nominal SNR of $-10 \mathrm{d B}$.