  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.
