Publication

Abstract : The availability of the radio spectrum is limited. To compromise the increasing demand for high data rate devices, this fixed spectrum need to be used efficiently. The existing challenge is that the larger portion of the licensed spectrum is underutilized. Cognitive Radio is a technology introduced to help to detect the radio spectrum is occupied or not. Spectrum sensing helps to detect the spectrum holes and provides high spectral resolution capability. This is done using sparse techniques such as Compressive sensing and Sparse Bayesian Learning techniques. Compressive Sensing algorithms such as Basis Pursuit and Orthogonal Matching Pursuit are analyzed. Based on the concept of Sparse Bayesian learning, an expectation maximization algorithm is introduced for spectrum sensing and recovery of the original transmitted signal in cognitive radio systems. Performance comparison is done between proposed algorithms and is validated using Register Transfer Level- Software Defined Radio. © 2019 IEEE.