Channel Energy Statistics Learning in Compressive Spectrum Sensing

Abstract

Spectrum sensing is a proactive way in cognitive radio systems to achieve dynamic spectrum access; and compressive spectrum sensing (CSS) techniques alleviate the demand for high-speed sampling in wideband spectrum sensing. Most existing literature discusses Neyman–Pearson channel energy detection and threshold adaption schemes to achieve an optimal performance of detection in a conventional non-compressive spectrum sensing scenario. However, in the CSS, it is found that the channel energy statistics and optimal threshold depend not only on noise energy but also on compression ratio, sparsity of spectrum, and nature of recovery algorithms. To investigate the channel energy statistics of recovered spectrum, we postulate a statistical model of channel energy for CSS and propose a learning algorithm based on a mixture model and expectation–maximization techniques. In addition, having verified the validity of the postulated model, we propose a practical threshold adaption scheme for CSS aiming to maintain constant false alarm rates in channel energy detection. In simulations, it is shown that the postulated channel energy statistic models with parameters learned by the proposed learning algorithm fit well with empirical distributions under circumstances of various channel models and recovery algorithms. Moreover, it is presented that the proposed threshold adaption scheme maintains the false alarm rate near the predefined constant, which in turn validates the postulated model.