I2-Minimization Recovery Algorithm for Compressive Sensing in Cognitive Radio Networks

Abstract

Compressive sensing (CS) is a novel sampling technique that can help Cognitive Radio (CR) for sampling the signals in an efficient way. This is done with taking in consideration that the spectrum is sparse in the frequency domain. Its operational way depends on using a suitable sensing matrix with the aid of an appropriate recovery algorithm. With respect to this point, this paper proposes a new recovery algorithm for determining the status of the different sub-channels in the spectrum. The proposed recovery algorithm is considered as a kind of optimization techniques since it depends on the use of the l 2 -minimization technique. To get the indices of the occupied sub-channels of the spectrum, the proposed recovery algorithm is accomplished with a new and simple extracted process. Comparing with one of the familiar greedy algorithms, Orthogonal Matching Pursuit (OMP), it can be seen that the proposed recovery algorithm has a better performance. Moreover, the results show that the proposed algorithm have high probability of detection at different Signal to Noise Ratio (SNR) for different number of the occupied sub-channels.