Deep Learning-Aided OFDM-Based Generalized Optical Quadrature Spatial Modulation

TL;DR

This paper introduces a deep learning-enhanced OFDM-based generalized optical quadrature spatial modulation method for MIMO optical wireless communications, significantly improving detection accuracy and system performance.

Contribution

It proposes a novel DNN-aided detection scheme for OFDM-based GOQSM, effectively mitigating error propagation and noise amplification issues in MIMO-OWC systems.

Findings

DNN-aided detection outperforms traditional methods

Enhanced system robustness and accuracy

Verified through comprehensive simulations

Abstract

In this paper, we propose an orthogonal frequency division multiplexing (OFDM)-based generalized optical quadrature spatial modulation (GOQSM) technique for multiple-input multiple-output optical wireless communication (MIMO-OWC) systems. Considering the error propagation and noise amplification effects when applying maximum likelihood and maximum ratio combining (ML-MRC)-based detection, we further propose a deep neural network (DNN)-aided detection for OFDM-based GOQSM systems. The proposed DNN-aided detection scheme performs the GOQSM detection in a joint manner, which can efficiently eliminate the adverse effects of both error propagation and noise amplification. The obtained simulation results successfully verify the superiority of the deep learning-aided OFDM-based GOQSM technique for high-speed MIMO-OWC systems.