  Massive spatial modulation (SM)-MIMO, which employs massive low-cost antennas
but few power-hungry transmit radio frequency (RF) chains at the transmitter,
is recently proposed to provide both high spectrum efficiency and energy
efficiency for future green communications. However, in massive SM-MIMO, the
optimal maximum likelihood (ML) detector has the prohibitively high complexity,
while state-of-the-art low-complexity detectors for conventional small-scale
SM-MIMO suffer from an obvious performance loss. In this paper, by exploiting
the structured sparsity of multiple SM signals, we propose a low-complexity
signal detector based on structured compressive sensing (SCS) to improve the
signal detection performance. Specifically, we first propose the grouped
transmission scheme at the transmitter, where multiple SM signals in several
continuous time slots are grouped to carry the common spatial constellation
symbol to introduce the desired structured sparsity. Accordingly, a structured
subspace pursuit (SSP) algorithm is proposed at the receiver to jointly detect
multiple SM signals by leveraging the structured sparsity. In addition, we also
propose the SM signal interleaving to permute SM signals in the same
transmission group, whereby the channel diversity can be exploited to further
improve the signal detection performance. Theoretical analysis quantifies the
performance gain from SM signal interleaving, and simulation results
demonstrate the near-optimal performance of the proposed scheme.
