Tensor-based framework for the prediction of frequency-selective time-variant MIMO channels

Abstract

In this contribution we propose a tensor-based framework for the prediction of time-variant frequency-selective multiple-input multiple-output (MIMO) channels from noisy channel estimates. This method performs the prediction in a transformed domain obtained via the higher order singular value decomposition (HOSVD), namely on the transformed tensor elements. This is followed by the inverse transformation of the predicted transformed tensor elements onto a basis corresponding to the signal subspace. To verify our strategy, we compare the results in terms of the normalized mean square error using a known prediction method, e.g., a Wiener filter, applied to the transformed tensor elements with the identical method applied directly to the channel coefficients. The results of our investigation show that the tensor-based prediction method outperforms the direct prediction method. Although we concentrate in this contribution on the prediction in the time domain, this framework can also be used for the estimation in other domains.