Robust Compressive Sensing of Multiband Spectrum with Partial and Incorrect Priors

Abstract

Compressive sensing has been applied in wideband spectrum sensing to achieve sub-Nyquist sampling. Prior information of the multiband spectrum occupancy, e.g. from geo-location database, can be utilized by compressive spectrum sensing (CSS) to enhance the sensing performance. However, these priors are prone to be partially missing and may also contain incorrect information. We hereby propose a CSS scheme aided by priors and robust to priors imperfections, and moreover, a novel and practical algorithm to provide robust channel sparsity estimation needed by the CSS scheme. Simulations show prominent enhancement of detection performance and lower iteration counts by employing priors in the proposed CSS scheme.