Blind Compressive Sensing forCooperative Cognitive Radio with Semi-Orthognal Regular Parity Check Matrix and l2-Minimization

Recently, Compressive sensing (CS) paradigm has been considered as an attractive sampling method comparing with Nyquist Sampling Theorem. This is due to its ability to have the same efficiency by using a smaller number of samples. Within the centralized Cognitive Radio (CR) network; it can be individually applied at each Secondary User (SU) which leads to reduce its collected samples into a small number of the measurements. In this paper, this reduction is recognized by using the semi-orthogonal Regular Parity Check (RPC) matrix as a measurement matrix of CS paradigm. By this way, the overall samples that are going to be processed at the Fusion Centre (FC) to detect the holes are also reduced. This detection is carried on by using the l
₂
- minimization as a CS recovery algorithm. The integration between the semi-orthogonal RPC matrix and the l
₂
-minimization recovery algorithm helps for reducing the complexity of the l
₂
- minimization recovery algorithm. Moreover, the results show that the used matrix enhances the ability of the l
₂
- minimization recovery algorithm to estimate the status of the sub-channels. This is done with high accuracies at different number of SUs for different Signal to Noise Ratio (SNR) and without any prior information about PUs' number.