Adaptive Compressive Spectrum Sensing Using a Deterministic Estimation Model for Wideband Cognitive Radios

Adaptive compressive spectrum sensing (ACSS) plays a vital role in cognitive radio networks due to its reduced sampling rate and power consumption. Most of the existing ACSS schemes focus on the improvement of spectrum sensing performance, but do not provide the deterministic assurance of sensing results. Hence, we propose an ACSS based on deterministic estimation model (ACSS-DEM) to provide the confidence level of the reconstructed signals. This is realized by deriving the closed-form expression of the cumulative distribution function (CDF) of reconstructed errors. Firstly, in each sensing interval of ACSS, we propose a novel signal reconstruction algorithm that incorporates the prior knowledge into ℓ_2,1-norm minimization of block-sparse signals. Secondly, a prior knowledge refining strategy is designed through convex geometry theory to further improve the reconstruction accuracy. Finally, the CDF of reconstruction error is derived and regarded as a stopping criteria for observation sample collection. The experiment results demonstrate that the proposed ACSS-DEM delivers optimal spectrum-sensing performance, offering over a 12.2% improvement in detection performance at a false alarm probability of 0.1, compared with other typical ACSS methods, e.g., ACSS-JL, ACSS-SOC, and ACSS-CV.