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.

翻译：高效的频道估计在全维多投入多输出通信系统中具有挑战性,特别是在具有混合数字-模拟结构的系统中。在压缩遥感框架下,这封信首先设计了一套基于球形Fibonacci网格的统一字典,以代表稀疏域内的频道,在三维波束空间中产生比传统词典较小的角差错。然后,开发了一种贝叶斯推论辅助的贪婪追踪算法,以估计频率域中的频道。最后,模拟结果表明,设计的字典和拟议的巴伊西亚频道估计都超过了基准方案,并实现了较低的正常平均平方位估计差。