Deep Learning-Aided Spatial Multiplexing with Index Modulation

TL;DR

This paper introduces a deep learning-based detection method for spatial multiplexing with index modulation in MIMO systems, achieving improved error performance with reduced complexity.

Contribution

It proposes a novel Deep-SMX-IM method combining zero-forcing detection and deep learning to enhance detection accuracy in MIMO systems with index modulation.

Findings

Significant error performance improvements over traditional ZF detection.

Reduced computational complexity due to subblock-based detection.

Effective learning of transmission characteristics in frequency and spatial domains.

Abstract

In this paper, deep learning (DL)-aided data detection of spatial multiplexing (SMX) multiple-input multiple-output (MIMO) transmission with index modulation (IM) (Deep-SMX-IM) has been proposed. Deep-SMX-IM has been constructed by combining a zero-forcing (ZF) detector and DL technique. The proposed method uses the significant advantages of DL techniques to learn transmission characteristics of the frequency and spatial domains. Furthermore, thanks to using subblockbased detection provided by IM, Deep-SMX-IM is a straightforward method, which eventually reveals reduced complexity. It has been shown that Deep-SMX-IM has significant error performance gains compared to ZF detector without increasing computational complexity for different system configurations.