Enabling Massive Index Modulation Systems via Combinatorics-Free Detection

TL;DR

This paper introduces a low-complexity message passing decoder for large-scale index modulation systems, enabling efficient detection without combinatorial complexity, thus enhancing spectral efficiency for future wireless systems.

Contribution

It proposes a novel Gaussian belief propagation-based decoder with a unit vector decomposition, reducing detection complexity in massive IM systems while maintaining near-optimal performance.

Findings

Decoding complexity is independent of combinatorial factors.

The method approaches the performance of state-of-the-art search methods.

Effective for systems with up to 96 antennas.

Abstract

Index modulation (IM) is one of the key enabling technologies for beyond fifth generation (B5G) and sixth generation (6G) wireless systems, attracting attention for its inherent energy and spectral efficiency resulting from conveying information through the indexation of the resources utilized in during signal transmission. However, a remaining critical bottleneck for large-scale IM is the consequently infeasible detection complexity of combinatoric order. Therefore in this article, in order to maximally reap the advantages of IM in large scenarios, we propose a novel message passing (MP) decoder designed under the Gaussian belief propagation (GaBP) framework exploiting a novel unit vector decomposition (UVD) of IM signals with purpose-derived novel probability distributions. The proposed method enjoys a low decoding complexity that is independent of previously prohibitive combinatorial factors, while still approach-ing the performance of unfeasible state-of-the-art (SotA) search-based methods. The effectiveness of the proposed approach is demonstrated via complexity analysis and numerical results for the exemplary piloted generalized quadrature spatial modulation (GQSM) systems of truly massive sizes (up to 96 antennas).