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

Application of Compressive Sensing and Belief Propagation for Channel Occupancy Detection in Cognitive Radio Networks Sadiq Jafar Sadiq Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2011 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 radio 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. This approach adds a new dimension to the scenario through ignoring specific portions of the time samples. The second algorithm detectes 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, less than the number of available channels. The energies of the filter outputs are then used as the compressed measurement to reconstruct the signal energy in each channel. The third algorithm employes two information theoretic algorithms (MDL and PDL) to find the sparsity level of a compressive signal and the last algorithm employs belief propagation for detecting the sparsity level. The performance of these algorithms is investigated through simulations.