Multidimensional Spectral Super-Resolution With Prior Knowledge With Application to High Mobility Channel Estimation

Abstract

The problem of estimating high-mobility channels is a special case of the general problem of recovering multi-dimensional (MD) complex sinusoids. This paper is concerned with estimation of multiple frequencies with prior knowledge from incomplete and/or noisy samples. Suppose that it is known a priori that the frequencies lie in some given intervals, we develop efficient super-resolution estimators by exploiting such prior knowledge based on frequency-selective (FS) atomic norm minimization. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to lie in given intervals. We then propose to solve the FS atomic norm minimization problems for the low-rank spectral tensor recovery by converting them into semidefinite programs based on the MD Vandermonde decomposition. We also develop fast solvers for solving these semidefinite programs via the alternating direction method of multipliers (ADMM), where each iteration involves a number of refinement steps to utilize the prior knowledge. Extensive simulation results are presented to illustrate the high performance of the proposed methods.