Bayesian Compressive Sampling Based Wideband Spectrum Sensing in Cognitive Radio Network using Wavelet Transform

Abstract

This paper deals with the implementation of sub Nyquist sampling for the efficient wideband spectrum sensing in cognitive radio network. Cognitive radio is a very promising technology in the field of wireless communication which has drastically changed the spectral dynamics through the opportunistic utilization of frequency band by the secondary users when it is not utilized by the primary users. The complexity of spectral detection strategy is reduced using the compressive sensing method. Bayesian technique is utilized in the compressive sampling to deal with uncertainty of the process and increase the speed of detection. This technique recovers the wideband signals even with few measurements via Laplace prior and Toeplitz matrix. Sparse signal recovery algorithm is used for the extraction of primary user frequency location. The condition of the detection of primary user even in the low regulated transmission from unlicensed user is been resolved in this paper through Wavelet transform. This approach enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation analysis is performed to verify the effectiveness of the proposed technique over the cognitive radio network.