Glrt-based cooperative spectrum sensing for cognitive radio with rank information

In a practical cognitive radio system, the exact knowledge of the primary signal space and the noise space is hard to obtain. However, the rank information of the signal space can be provided by the primary system or it can be estimated by the secondary users. Such information can help to efficiently extract the signal space embedded in the sample covariance matrix of the received signals. In this paper, we investigate generalized likelihood ratio test (GLRT) methods that use the a priori rank information. The developed test statistics are of a general form with an additional rank constraint on the signal space. The effectiveness of the proposed methods is verified by numerical results.