LAUSR

Multiple-input multiple-output technology brings new challenges for detecting signal parameters in some intelligent systems. An accurate estimation of the transmit-antenna number is the prerequisite for estimating other signal parameters. Employing random matrix theory, we propose two hypothesis testing based algorithms to blindly estimate the transmit-antenna number. By exploiting hypothesis testing, the blind estimation problem is converted to a location problem, which seeks the location of the boundary between the signal subspace and the noise subspace of the received sample covariance matrix. Then, the estimated transmit-antenna number is determined by the dimension of the signal subspace. Extensive simulations verify that the two proposed algorithms perform better under a wide range of signal-to-noise ratios and sample lengths, compared with conventional algorithms based on Akaike information criterion, minimum description length, and predicted eigenvalue threshold.