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Deep learning-based detection scheme for visible light communication with generalized spatial modulation
Author(s) -
Tengjiao Wang,
Fang Yang,
Jian Song
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.404463
Subject(s) - computer science , visible light communication , scheme (mathematics) , artificial neural network , modulation (music) , deep learning , detection theory , artificial intelligence , signal processing , pattern recognition (psychology) , detector , optics , telecommunications , light emitting diode , mathematics , mathematical analysis , philosophy , radar , physics , aesthetics
In this paper, a deep learning-based detection scheme is proposed for the visible light communication (VLC) systems using generalized spatial modulation (GenSM). In the proposed detection scheme, a deep neural network consisting of several neural layers is applied to detect the received signals. By integrating the signal processing modules of the conventional detection schemes into one deep neural network, the proposed scheme is able to extract the information bits from the received signals efficiently. After offline training, the proposed detection scheme can serve as a promising detection method for the VLC system with GenSM. Simulation results validate that the proposed detection scheme is capable of achieving superior detection error performance than conventional detection schemes at acceptable complexity.

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