  In this paper, we study the low-complexity channel reconstruction methods for
downlink precoding in massive MIMO systems. When the user is allocated less
streams than the number of its antennas, the BS or user usually utilizes the
singular value decomposition (SVD) factorizations to get the effective
channels, whose dimension is equal to the num of streams. This process is
called channel reconstruction in BS for TDD mode. However, with the increasing
of antennas in BS, the computation burden of SVD is becoming incredibly high.
As a countermeasure, we propose a series of novel low-complexity channel
reconstruction methods for downlink zero-forcing precoding (ZF). We adopt
randomized algorithms to construct an approximate SVD, which could reduce the
dimensions of the matrix, especially when approximating an input matrix with a
low-rank element. Besides, this method could automatically modify the
parameters to adapt arbitrary number demand of streams from users. The
simulation results show that the proposed methods only cost less than 30% float
computation than the traditional SVD-ZF method, while keeping nearly the same
performance of 1Gbps with 128 BS antennas.
