Bayesian Compressive Channel Estimation for Hybrid Full-Dimensional MIMO Communications

TL;DR

This paper introduces a Bayesian compressive sensing approach with a spherical Fibonacci grid dictionary for efficient channel estimation in hybrid 3D MIMO systems, outperforming traditional methods.

Contribution

It proposes a novel spherical Fibonacci grid dictionary and a Bayesian inference-based greedy algorithm for improved channel estimation in hybrid 3D MIMO systems.

Findings

Lower normalized mean squared error in channel estimation.

Outperforms benchmark schemes in simulations.

Dictionary design reduces angular errors.

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. 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 channel estimation outperform the benchmark schemes and attain a lower normalized mean squared error of channel estimation.