Structured Tensor Decomposition-Based Channel Estimation for Wideband Millimeter Wave MIMO

In this paper, we focus on the channel estimation of millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing systems with hybrid analog-digital structures. By exploiting the sparse characteristics of mmWave channels, we transform the channel estimation issue to the recovery problem of multi-path parameters, e.g., angle of arrival/departure, time delay and path gain. We formulate the downlink training signals as a third-order low-rank tensor, which fits a canonical polyadic (CP) model with three factor matrices containing the channel parameters. We further exploit the Vandermonde structure of the factor matrix and develop a structured CP decomposition-aided channel estimation algorithm, which leverages standard linear algebra and avoids random initialization and iterations. The simulation results show that the proposed scheme outperforms the traditional strategies in terms of accuracy, complexity and robustness.