  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.
