为了明确室内不同场景、不同频段以及不同天线数目下大规模三维多输入多输出(Massive 3D-MIMO,Massive Three-Dimensional Multiple-Input Multiple-Output)的差异,本文搭建了一种3D-MIMO信道测量平台,对典型室内非可视(NLOS,Non Line o...为了明确室内不同场景、不同频段以及不同天线数目下大规模三维多输入多输出(Massive 3D-MIMO,Massive Three-Dimensional Multiple-Input Multiple-Output)的差异,本文搭建了一种3D-MIMO信道测量平台,对典型室内非可视(NLOS,Non Line of Sight)环境下大规模3D-MIMO无线信道进行测量,利用三维逆快速傅里叶变换(Inverse Fast Fourier Transform,IFFT)对各个场景下的信道特性进行了波数域的表征。结果表明,在NLOS信道下,3D-MIMO的空间分辨率依然取决于天线阵列的大小,且空间分辨能力几乎不受频段变化的影响。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
文摘为了明确室内不同场景、不同频段以及不同天线数目下大规模三维多输入多输出(Massive 3D-MIMO,Massive Three-Dimensional Multiple-Input Multiple-Output)的差异,本文搭建了一种3D-MIMO信道测量平台,对典型室内非可视(NLOS,Non Line of Sight)环境下大规模3D-MIMO无线信道进行测量,利用三维逆快速傅里叶变换(Inverse Fast Fourier Transform,IFFT)对各个场景下的信道特性进行了波数域的表征。结果表明,在NLOS信道下,3D-MIMO的空间分辨率依然取决于天线阵列的大小,且空间分辨能力几乎不受频段变化的影响。
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.
