Wideband Spectrum Sensing in Dynamic Spectrum Access Systems Using Bayesian Learning

Abstract

Abstract The commercialization and growth of Cognitive radio technology demand a spectrum sensing system that reacts in real-time to smart resolution, unlike the current mobile standards that do not have inbuilt features. Spectrum utilization is heterogeneous in practice. Spectrum utilization in various bands shares the same sparsity level. A heterogeneous wideband will be grouped into an inherited block structure to design an efficient sub-Nyquist spectrum sensing technique. Block sparse Bayesian learning is used for the recovery of signals. Two methods adopted are 1) With prior knowledge of block partition and 2) Without knowledge of block partition. These methods will result in an a-posterior estimated recovery of signal. The algorithm has been developed to sense the wideband to identify its vacant spectrum irrespective of the vacant band’s sparsity level and location. Block Sparse Bayesian Learning (BSBL) method can provide good performance at all Signal to Noise ratio (SNR) compared to the state-of-art methods.