Analysis of smoothing techniques for subspace estimation with application to channel estimation

In this paper, we present an 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 chosen properly, leads to dramatic performance breakdown of subspace-based methods. We 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 used window size. Through simulations we show that the magnitude of this eigenvalue is of particular importance for estimating the signal subspace and the entailing performance of the channel estimator.