Spatial Rank Estimation in Cognitive Radio Networks With Uncalibrated Multiple Antennas
Spectrum sensing is a key component of the Cognitive Radio paradigm. Multi-antenna detectors can exploit different spatial features of primary signals in order to boost detection performance and robustness in very low signal-to-noise ratios. However, in several cases these detectors require additional information, such as the rank of the spatial covariance matrix of the received signal. In this paper, the authors study the problem of estimating this rank under Gaussianity assumption using an uncalibrated receiver, i.e., with different (unknown) noise levels at each of the antennas.