  This paper investigates the combination of parametric channel estimation with
minimum mean square error (MMSE) estimation. We propose a direction-of-arrival
(DoA)-aided two-stage channel estimation technique that utilizes the
decomposition of wireless communication channels into a line-of-sight (LoS)
path and its orthogonal subspace. After estimating the channel along the
dominant direction, we utilize a Gaussian mixture model to estimate the
conditionally Gaussian distributed random vector, which represents the
multipath propagation. The proposed two-stage estimator allows pre-computing
the respective estimation filters, tremendously reducing the computational
complexity. Numerical simulations with typical channel models depict the
superior performance of our proposed two-stage estimation approach compared to
state-of-the-art methods.
