Robust Super-resolution Frequency Division Duplex (FDD) Channel Estimation

Abstract

Channel estimation is the process of estimating channel parameters from the received samples, which are corrupted by noise. Most of the conventional methods are designed for noise-free or Gaussian noise environment. However, impulsive noise, which is also referred to as outliers, is common in practice and performance of the conventional algorithms degrades in the presence of outliers. In this paper, we propose a robust super-resolution channel estimation algorithm to deal with outliers by replacing ℓ2-norm constraints with ℓ1-norm constraints to enhance robustness to outliers and solving an improved convex program to obtain the channel parameters, the angles and time delays then are estimated jointly. Simulation results show that the proposed robust super-resolution channel estimation algorithm outperforms the traditional methods and show great robustness to the outliers.