HOSVD based multidimensional parameter estimation for massive MIMO system from incomplete channel measurements

The dominant multipath components for massive multiple-input multiple-output systems can be described using geometry-based channel models with R-dimensional (R-D) parameters. These parameters are crucial for channel correlation acquisition, which is a prerequisite for many technical challenges. In this paper, we consider higher-order singular value decomposition based R-D channel modeling parameter estimation from incomplete measurements. Incomplete higher-order orthogonality iteration algorithm can be utilized to solve the problem, which simultaneously achieves tensor recovery and tensor decomposition. After obtaining the signal or noise subspace, the parameters of interest can be estimated by using subspace methods.