  We investigate the problem of spectrum sensing in cognitive radios (CRs) when
the receivers are equipped with a large array of antennas. We propose and
derive three detectors based on the concept of linear spectral statistics (LSS)
in the field of random matrix theory (RMT). These detectors correspond to the
generalized likelihood ratio (GLR), Frobenius norm, and Rao tests employed in
conventional multiple antenna spectrum sensing (MASS). Subsequently, we compute
the Gaussian distribution of the proposed detectors under the noise-only
hypothesis, leveraging the central limit theorem (CLT) applied to
high-dimensional random matrices. We evaluate the performance of the proposed
detectors and analyze the impact of the number of antennas and samples on their
efficacy. Furthermore, we assess the accuracy of the theoretical results by
comparing them with simulation outcomes. The simulation results provide
evidence that the proposed detectors exhibit efficient performance in wireless
networks featuring large array antennas. These detectors find practical
applications in diverse domains, including massive MIMO wireless
communications, radar systems, and astronomical applications.
