@article {RamirezVazquezTSP11, title = {Detection of Rank-{P} Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas}, journal = {IEEE Trans. on Signal Processing}, volume = {59}, number = {8}, year = {2011}, month = {08/2012}, pages = {3764-3774}, abstract = {
Spectrum sensing is a key component of the Cognitive\ Radio paradigm. Typically, primary signals have to be\ detected with uncalibrated receivers at signal-to-noise ratios\ (SNRs) well below decodability levels. Multiantenna detectors\ exploit spatial independence of receiver thermal noise to boost\ detection performance and robustness. We study the problem
of detecting a Gaussian signal with rank-P unknown spatial
covariance matrix in spatially uncorrelated Gaussian noise with
unknown covariance using multiple antennas. The generalized
likelihood ratio test (GLRT) is derived for two scenarios. In the
first one, the noises at all antennas are assumed to have the same\ (unknown) variance, whereas in the second, a generic diagonal\ noise covariance matrix is allowed in order to accommodate\ calibration uncertainties in the different antenna frontends. In\ the latter case, the GLRT statistic must be obtained numerically,\ for which an efficient method is presented. Furthermore, for\ asymptotically low SNR, it is shown that the GLRT does\ admit a closed form, and the resulting detector performs well\ in practice. Extensions are presented in order to account for\ unknown temporal correlation in both signal and noise, as well\ as frequency-selective channels.
}, keywords = {cognitive radio, dynacs, spectrum sensing}, doi = {10.1109/TSP.2011.2146779}, author = {David Ram{\'\i}rez and Gonzalo V{\'a}zquez-Vilar and R. L{\'o}pez-Valcarce and Javier V{\'\i}a and Ignacio Santamar{\'\i}a} }