Blind compressive sensing for cognitive radio networks using spectrum segmentation and l2-minimization recovery

Compressive Sensing (CS) is a signal reduction and reconstruction paradigm. It can be applicable in the fields which processes the sparse signals. One of them is Cognitive Radio (CR) system that senses the frequency domain sparse spectrum. By CS, Fusion Center (FC) of CR network can detect the status of the different sub-channels by a small number of measurements. This is done using a suitable sensing matrix with the aid of an appropriate recovery algorithm. In this paper, a proposed spectrum segmentation technique together with l
₂
-minimization, as a CS recovery algorithm, are used to estimate the statues of the different sub-channels. This is achieved without a prior knowledge about the number of the licensed users, which is known as blind CS. The results show that the proposed technique able to determine the status of the sub-channels with high accuracies at different Signal to Noise Ratios (SNR) and different number of CR users for different sparsity levels.