Application of Compressive Sensing and Belief Propagation for Channel Occupancy Detection in Cognitive Radio Networks

Wide-band spectrum sensing is an approach for finding spectrum holes within a wideband signal with less complexity/delay than the conventional approaches. In this thesis, we propose four different algorithms for detecting the holes in a wide-band spectrum and finding the sparsity level of compressive signals. The first algorithm estimates the spectrum in an efficient manner and uses this estimation to find the holes. The second algorithm detects the spectrum holes by reconstructing channel energies instead of reconstructing the spectrum itself. In this method, the signal is fed into a number of filters. The energies of the filter outputs are used as the compressed measurement to reconstruct the signal energy. The third algorithm employs two information theoretic algorithms to find the sparsity level of a compressive signal and the last algorithm employs belief propagation for detecting the sparsity level.MAS