Bayesian Inference-Aided Channel Estimation for Hybrid Full-Dimensional MIMO Systems

Abstract

Efficient channel estimation is challenging in full-dimensional multiple-input multiple-output communication systems, particularly in those with hybrid digital-analog architectures. Under a compressive sensing framework, this letter first designs a uniform dictionary based on a spherical Fibonacci grid to represent channels in a sparse domain, yielding smaller angular errors in three-dimensional beamspace than traditional dictionaries and consistent estimation in different directions. Then, a Bayesian inference-aided greedy pursuit algorithm is developed to estimate channels in the frequency domain. Finally, simulation results demonstrate that both the designed dictionary and the proposed Bayesian inference-aided channel estimation outperform the benchmark schemes and attain a lower normalized mean squared error of channel estimation.