Near-Optimal Signal Detector Based on Structured Compressive Sensing for Massive SM-MIMO

Massive spatial-modulation multiple-input multiple-output (SM-MIMO) with high spectrum efficiency and energy efficiency has recently been proposed for future green communications. However, in massive SM-MIMO, the optimal maximum-likelihood detector has the high complexity, whereas state-of-the-art low-complexity detectors for 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 signal detection performance. Theoretical analysis quantifies the gain from SM signal interleaving, and simulation results verify the near-optimal performance of the proposed scheme.