信道估计是通过对基站接收信号和用户端发送的已知导频序列进行处理获得。二维嵌套阵列可以节约系统的成本并且得到大规模MIMO(multiple-input multiple-output)天线阵列的性能,然而由于二维嵌套阵列的结构不规整,直接对基站接收信号进...信道估计是通过对基站接收信号和用户端发送的已知导频序列进行处理获得。二维嵌套阵列可以节约系统的成本并且得到大规模MIMO(multiple-input multiple-output)天线阵列的性能,然而由于二维嵌套阵列的结构不规整,直接对基站接收信号进行处理具有一定的难度。本文提出一种基于2D-DFT(two-dimensional Discrete Fourier Transform)的信道重构算法,首先对接收信号做自相关处理转化为连续差分阵列的接收信号,其次通过2D-DFT估计出用户的初始DOA(the direction of arrival),然后利用角度旋转技术增强DOA估计实现超分辨率估计,再根据精确的DOA估计通过传统的LS(least squares)估计方法估计出信道增益;最后重构出用户的信道。数值仿真验证了算法的有效性。展开更多
In this paper, we study the low-complexity channel reconstruction methods for downlink precoding in massive multiple-Input multiple-Output(MIMO) systems. When the user is allocated less streams than the number of its ...In this paper, we study the low-complexity channel reconstruction methods for downlink precoding in massive multiple-Input multiple-Output(MIMO) systems. When the user is allocated less streams than the number of its antennas, the base station(BS) or user usually utilizes the singular value decomposition(SVD) to get the effective channels, whose dimension is equal to the number of streams. This process is called channel reconstruction and done in BS for time division duplex(TDD) mode. However, with the increasing of antennas in BS, the computation burden of SVD is getting incredible. Here, we propose a series of novel low-complexity channel reconstruction methods for downlink precoding in 3D spatial channel model. We consider different correlations between elevation and azimuth antennas, and divide the large dimensional matrix SVD into two kinds of small-size matrix SVD. The simulation results show that the proposed methods only produce less than 10% float computation than the traditional SVD zero-forcing(SVD-ZF) precoding method, while keeping nearly the same performance of 1Gbps.展开更多
文摘信道估计是通过对基站接收信号和用户端发送的已知导频序列进行处理获得。二维嵌套阵列可以节约系统的成本并且得到大规模MIMO(multiple-input multiple-output)天线阵列的性能,然而由于二维嵌套阵列的结构不规整,直接对基站接收信号进行处理具有一定的难度。本文提出一种基于2D-DFT(two-dimensional Discrete Fourier Transform)的信道重构算法,首先对接收信号做自相关处理转化为连续差分阵列的接收信号,其次通过2D-DFT估计出用户的初始DOA(the direction of arrival),然后利用角度旋转技术增强DOA估计实现超分辨率估计,再根据精确的DOA估计通过传统的LS(least squares)估计方法估计出信道增益;最后重构出用户的信道。数值仿真验证了算法的有效性。
基金supported by the National High Technology Research and Development Program of China(863 Program)(Grant No.2014AA01A705)National Science and Technology Major Project of China(Grant No.2015ZX03001034)
文摘In this paper, we study the low-complexity channel reconstruction methods for downlink precoding in massive multiple-Input multiple-Output(MIMO) systems. When the user is allocated less streams than the number of its antennas, the base station(BS) or user usually utilizes the singular value decomposition(SVD) to get the effective channels, whose dimension is equal to the number of streams. This process is called channel reconstruction and done in BS for time division duplex(TDD) mode. However, with the increasing of antennas in BS, the computation burden of SVD is getting incredible. Here, we propose a series of novel low-complexity channel reconstruction methods for downlink precoding in 3D spatial channel model. We consider different correlations between elevation and azimuth antennas, and divide the large dimensional matrix SVD into two kinds of small-size matrix SVD. The simulation results show that the proposed methods only produce less than 10% float computation than the traditional SVD zero-forcing(SVD-ZF) precoding method, while keeping nearly the same performance of 1Gbps.
