波束空间大规模多输入多输出（Massive B-MIMO）是未来实现密集蜂窝小区通信的高谱效、高功效关键技术之一。现有的波束空间信道估计方法，由于未能充分挖掘系统的多维度结构，在基站天线数和/或移动用户数较大时，不能有效解决导频开销过大的问题。本项目从系统低秩特性和多维特征信息的内在隐含结构出发，提出融合多维特征信息的波束空间信道估计方法，研究内容包括：挖掘系统多维特征信息的相关性，建立融合多维特征信息的接收导频信号张量模型；设计基于张量分解的波束空间信道估计方法，研究估计参数的克拉美罗界性能；分析接收导频信号张量分解唯一性条件，设计基于导频开销最小化的训练序列。本项目拟通过对上述问题的研究，解决经典波束空间信道估计方法导频开销过大对系统频谱效率和功率效率提升的制约，为Massive B-MIMO技术的研究和工程实现提供理论参考。

Beamspace massive multiple-input-multiple-output (Massive B-MIMO) is one of the key technologies for achieving high spectral efficiency and power efficiency in dense cell communication in the future. Because the multi-dimensional structure of the system cannot be fully exploited, the existing beamspace channel estimation methods cannot effectively solve the problem of excessive pilot overhead when the number of base station antennas and/or the number of mobile users is large. This project starts from the system's low rank characteristic and the inherent implicit structure of multi-dimensional characteristic information and proposes a beamspace channel estimation method based on multi-dimensional characteristic information. The contents of this project include: mining the correlation of multi-dimensional characteristic information and establishing a receiving pilot signal tensor model that combines multi-dimensional characteristic information; designing the beamspace channel estimation method based on tensor decomposition, study the performance of Cramer-Rao Lower Bound of the estimated parameters; the uniqueness of the tensor decomposition of the received pilot signal is analyzed and the training sequence based on minimization of pilot overhead is designed. The project intends to solve the problems that the traditional beamspace channel estimation method has too much pilot overhead to improve the spectrum efficiency and power efficiency of the system, provides theoretical reference for the research and engineering implementation of Massive B-MIMO technology.