Exact quickest spectrum sensing algorithms for eigenvalue-based change detection

We study a collaborative quickest detection scheme that uses a function of the eigenvalues of the sample covariance matrix for a spectrum sensing system with a fusion center. A simple model consisting of one potentially present primary user (PU), which utilizes phase shift keying (PSK), and the standard additive white Gaussian noise (AWGN) assumption is considered. Here, for both detection hypothesis, the sample covariance matrix follows a Wishart distribution. For K = 2 collaborating secondary users (SUs), the probability distribution function (PDF) of the maximum-minimum eigenvalue (MME) test statistic can be derived analytically under both hypotheses, allowing us to develop exact quickest detection algorithms for known and unknown SNR. We analyze the two types of change detection problems in spectrum sensing, i.e., the channel becoming free when it was occupied before and vice versa. Performance evaluation is done by evaluating bounds and by comparing the presented quickest detection algorithms with the traditional block detection scheme.