SVD-based estimation for reduced-rank MIMO channel

Channel estimation schemes based on SVD (singular value decomposition) are proposed for reduced-rank multi-input-multi-output (MIMO) systems, where instead of estimating each entry of the channel matrix, the singular spaces and singular values are estimated. When the channel rank is fixed and known, the maximum-likelihood (ML) estimator is derived. When the channel rank is random and unknown, a threshold-based rank detection algorithm using the singular values is adopted. In finding the threshold, a lower bound on the correct detection probability is derived and the threshold is chosen to maximize the lower bound. Simulations show that the SVD-based estimation achieves lower MSE and higher capacity than the entry-based estimation for both cases.