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

TL;DR

This paper introduces a low-complexity structured compressive sensing-based detector for massive SM-MIMO systems, leveraging structured sparsity and signal interleaving to achieve near-optimal detection performance with reduced complexity.

Contribution

It proposes a novel grouped transmission scheme and a structured subspace pursuit algorithm to exploit structured sparsity, significantly improving detection in massive SM-MIMO.

Findings

Achieves near-optimal detection performance in massive SM-MIMO.

Exploits structured sparsity and channel diversity for performance enhancement.

Provides theoretical analysis and simulation validation.

Abstract

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.