Compressive Sensing Single-User Signal Detection in Massive MIMO Systems with Spatial Modulation

In this chapter, a low-complexity signal detector based on structured CS is introduced to improve the signal detection performance in massive SM-MIMO systems. The goal is to address the high complexity of the optimal maximum likelihood (ML) detector in massive SM-MIMO, while also avoiding the performance loss associated with state-of-the-art low-complexity detectors for small-scale SM-MIMO. The adopted signal detector leverages the structured sparsity of multiple SM signals. We start by introducing a grouped transmission scheme at the transmitter, where multiple SM signals in several continuous time slots are grouped to carry the common spatial constellation symbol. This grouping introduces the desired structured sparsity. At the receiver, a structured subspace pursuit (SSP) algorithm is introduced to jointly detect multiple SM signals by leveraging the structured sparsity. Additionally, this chapter introduces SM signal interleaving to permute SM signals in the same transmission group. This allows for the exploitation of channel diversity to further improve the signal detection performance. Theoretical analysis is used to quantify the gain from SM signal interleaving, and simulation results verify the near-optimal performance of the considered scheme.