A unified framework for GLRT-based spectrum sensing of signals with covariance matrices with known eigenvalue multiplicities

In this paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigen-value multiplicities. We derive the generalized likelihood-ratio test (GLRT) for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio and show that the GLRT for these applications, of which some are already known, are special cases of the general result.