Semi-Blind Millimeter-Wave Channel Estimation Using Atomic Norm Minimization

Abstract

Exploiting the inherent sparsity of millimeter-wave channels, the channel estimation problem is formulated as an atomic norm minimization that enhances sparsity in the continuous angles of arrival and departure. A semi-blind channel estimator is developed to track the time-varying channel dynamics, which is formulated as a non-convex problem. To solve the formulated channel estimation problem, we develop a computationally efficient conjugate gradient descent method based on non-convex factorization which restricts the search space to low-rank matrices. Simulation results are presented to illustrate the superior channel estimation performance of the proposed algorithms compared with the existing compressed-sensing-based estimators with finely quantized angle grids.