Off-Grid Sparse Bayesian Learning-Based Channel Estimation for MmWave Massive MIMO Uplink

摘要

In this letter, an angle domain off-grid channel estimation algorithm for the uplink millimeter wave (mm Wave) massive multiple-input and multiple-output systems is proposed. By exploiting spatial sparse structure in mmWave channels, the proposed method is capable of identifying the angles and gains of the scatterer paths. Comparing the conventional channel estimation methods for mmWave systems, the proposed method achieves better performance in terms of mean square error. Numerical simulation results are provided to verify the superiority of the proposed algorithm.