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arXiv · 2106.12770

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

Chen Chen·Lin Zeng·Xin Zhong·Shu Fu·Min Liu·Pengfei Du
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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.

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Chen Chen, Lin Zeng, Xin Zhong, Shu Fu, Min Liu, Pengfei Du. 2021-06-24. Deep Learning-Aided OFDM-Based Generalized Optical Quadrature Spatial Modulation. https://doi.org/10.1109/jphot.2021.3129541

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