Performance Analysis of Robust GRCR Based Spectrum Detector Using Compressed Sensing with Non-reconstruction Model

In cognitive radio applications, a robust detector is essentially required for the determination of spectrum sensing. Gerschgorin radii and centers ratio (GRCR) detector is a robust detector for cooperative spectrum sensing techniques. Covariance matrix generates the test statistics for signal established from one or supplementary sources. Though the method is robust against noise uncertainty, it is not suitable for wideband sensing due to the complexity associated with the computation of covariance matrix. To tackle this challenge of extensive communication cost and high processing time complexity, an efficient GRCR detector using compressive sensing with non-reconstruction is proposed here. This method introduces relevance for multiple received signals by using same measurement matrix to all received signals. Computational complexity is analysed and the proposed method is compared with the existing method through ROC simulations, and it shown that the proposed method performs better even in the low SNR range of -20 dB. Throughput analysis is validated through simulations.