Analysis of Smoothing Techniques for Subspace Estimation with Application to Channel Estimation

In this paper, we present a thorough investigation on the impact of spatial smoothing and forward-backward averaging techniques for subspace-based channel estimation. The spatial smoothing technique requires the selection of a window size, which, if not selected properly, leads to dramatically performance breakdown of the subspace-based methods. We aim to provide an explanation of the performance drop for certain window sizes and subsequently an understanding of a proper window size selection. In particular, we describe the behavior of the magnitude of the least signal eigenvalue as a function of the window size employed. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the subspace and the entailing performance of the channel estimator.