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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., Comment: 5 pages, 5 figures, submitted for possible publication

In this paper, a novel pattern synthesis algorithm for a wideband smart antenna system is presented. Based on the adaptive theory, the proposed algorithm can control the pattern precisely in both mainlobe and sidelobe region. With the novel algorithm, it is efficient to obtain a frequency-invariant pattern over the whole frequency band. Simulation results illustrate the effectiveness of the proposed algorithm