Multiband Detection for Spectrum Sensing: A Multistage Wiener Filter Perspective
In the recent years, spectrum scarcity becomes an urgent issue due to the emergence of wireless services. The effective utilization of spectrum white space has gained significant research interests. Cognitive radio techniques have been paid much attention to the television white space. This paper raises a multiband spectrum sensing scheme to detect the spectrum white space which is not limited to television bands. When performing spectrum sensing, our approach operates over the total frequency bands simultaneously rather than a single band each time. By applying the idea of multistage Wiener filter to Gerschgorin disk estimator, our approach jointly makes the decision. In this way, the proposed method is able to capture the signal information and suppress the additive noise, which brings about an enhanced detection performance. Distinct from the classical methods, the proposed scheme requires neither noise power estimation nor prior knowledge of primary user signal, thereby being robust to noise uncertainty and suitable for blind detection. On the contrary, in the context of noise uncertainty, noise variance has no access to accurate estimation, inducing an imprecise decision threshold, which severely deteriorates the detection performance. Besides, our method avoids the estimation of covariance matrix as well as eigenvalue decomposition, and thus achieves a low computational complexity. This paper presents simulations under various conditions to verify the performance of the proposed scheme and the results show that it is superior to the existing sensing algorithms.
multiband detection spectrum sensing multistage wiener filter spectrum white space noise uncertainty