Blind estimation of SIMO channels using a tensor-based subspace method

In this paper, we introduce a tensor-based subspace method for solving the blind channel estimation problem in a single-input multiple-output (SIMO) system. Since the measurement data is multidimensional, previously proposed blind channel estimation methods require stacking the multiple dimensions into one highly structured vector and estimate the signal subspace via a singular value decomposition (SVD) of the correlation matrix of the measurement data. In contrast to this, we define a 3-way measurement tensor of the received signals and obtain the signal subspace via a multidimensional extension known as Higher-Order SVD (HOSVD). This allows us to exploit the structure inherent in the measurement data and leads to improved estimates of the signal subspace. Numerical simulations demonstrate that the proposed method outperforms previously proposed subspace based blind channel estimation methods in terms of the channel estimates accuracy. Furthermore, we show that the accuracy of the estimations is significantly improved by employing overlapping observed data windows at the receiver.