Enabling Energy-Efficiency in Massive-MIMO: A Scalable Low-Complexity Decoder for Generalized Quadrature Spatial Modulation

TL;DR

This paper introduces a scalable low-complexity decoder for generalized quadrature spatial modulation in massive MIMO systems, enabling high energy and spectral efficiency with manageable decoding complexity.

Contribution

A novel vectorized Gaussian belief propagation decoder that maintains constant complexity regardless of the combinatorial activation set size in GQSM.

Findings

Decoder complexity is independent of the combinatorial factor.

Simulation results up to 32 antennas demonstrate high performance.

The approach enables practical high-efficiency mMIMO systems.

Abstract

Generalized quadrature spatial modulation (GQSM) schemes are known to achieve high energy- and spectral- efficiencies by modulating information both in transmitted symbols and in coded combinatorial activations of subsets of multiple transmit antennas. A challenge of the approach is, however, the decoding complexity which scales with the efficiency of the scheme. In order to circumvent this bottleneck and enable high-performance and feasible GQSM in massive multiple-input multiple-output (mMIMO) scenarios, we propose a novel decoding algorithm which enjoys a complexity order that is independent of the combinatorial factor. This remarkable feature of the proposed decoder is a consequence of a novel vectorized Gaussian belief propagation (GaBP) algorithm, here contributed, whose message passing (MP) rules leverage both pilot symbols and the unit vector decomposition (UVD) of the GQSM signal structure. The effectiveness of the proposed UVD-GaBP method is illustrated via computer simulations including numerical results for systems of a size never before reported in related literature (up to 32 transmit antennas), which demonstrates the potential of the approach in paving the way towards high energy and spectral efficiency for wireless systems in a truly mMIMO setting.