Compressed Correlation-Matching for Spectrum Sensing in Sparse Wideband Regimes
In this paper, the authors consider a novel Compressed Correlation-Matching (CCM) approach for spectrum sensing of wideband sparse signals. They derive a general closed-form estimate of the wideband sparse signal level from compressed observations, while providing physical interpretation of the problem. The formulation allows straightforward application to signal processing problems of interest, such as Generalized Likelihood Ratio Test (GLRT) spectrum sensing for wideband cognitive radio. Simulation results are reported to assess the behavior of the CCM method.