  Large-scale multiple-input multiple-output (MIMO) with high spectrum and
energy efficiency is a very promising key technology for future 5G wireless
communications. For large-scale MIMO systems, accurate channel state
information (CSI) acquisition is a challenging problem, especially when each
user has to distinguish and estimate numerous channels coming from a large
number of transmit antennas in the downlink. Unlike the conventional orthogonal
pilots whose pilot overhead prohibitively increases with the number of transmit
antennas, we propose a spectrum-efficient superimposed pilot design for
downlink large-scale MIMO scenarios, where frequency-domain pilots of different
transmit antennas occupy the completely same subcarriers in the freqency
domain. Meanwhile, spatial-temporal common sparsity of large-scale MIMO
channels motivates us to exploit the emerging theory of structured compressive
sensing (CS) for reliable MIMO channel estimation, which is realized by the
proposed structured subspace pursuit (SSP) algorithm to simultaneously recover
multiple channels with low pilot overhead. Simulation results demonstrate that
the proposed scheme performs well and can approach the performance bound.
