Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System

Sujan Rajbhandari; Hyunchae Chun; Grahame  Faulkner; Harald Haas; Enyuan Xie; Jonathan J. D.  McKendry; Johannes  Herrnsdorf; Erdan  Gu; Martin D.  Dawson; Dominic O’Brien

doi:10.1109/LPT.2019.2909139

Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System

Sujan Rajbhandari, Hyunchae Chun, Grahame Faulkner, Harald Haas, Enyuan Xie, Jonathan J. D. McKendry, Johannes Herrnsdorf, Erdan Gu, Martin D. Dawson, Dominic O’Brien

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29 Citations (Scopus)

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Abstract

The limited bandwidth of white light-emitting diode (LED) limits the achievable data rate in a visible light communication (VLC) system. A number of techniques, including multiple-input-multiple-output (MIMO) system, are investigated to increase the data rate. The high-speed optical MIMO system suffers from both spatial and temporal cross talks. The spatial cross-talk is often compensated by the MIMO decoding algorithm, while the temporal cross talk is mitigated using an equalizer. However, the LEDs have a non-linear transfer function and the performance of linear equalizers are limited. In this letter, we propose a joint spatial and temporal equalization using an artificial neural network (ANN) for an MIMO-VLC system. We demonstrate using a practical imaging/non-imaging optical MIMO link that the ANN-based joint equalization outperforms the joint equalization using a traditional decision feedback as ANN is able to compensate the non-linear transfer function as well as cross talk.

Original language	English
Article number	8681630
Pages (from-to)	821 - 824
Number of pages	4
Journal	IEEE Photonics Technology Letters
Volume	31
Issue number	11
Early online date	4 Apr 2019
DOIs	https://doi.org/10.1109/LPT.2019.2909139
Publication status	Published - 1 Jun 2019

Bibliographical note

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

artificial neural network
joint equalization
multiple input multiple output
non-linear transfer function
Visible light communications

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering

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10.1109/LPT.2019.2909139

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abstract = "The limited bandwidth of white light-emitting diode (LED) limits the achievable data rate in a visible light communication (VLC) system. A number of techniques, including multiple-input-multiple-output (MIMO) system, are investigated to increase the data rate. The high-speed optical MIMO system suffers from both spatial and temporal cross talks. The spatial cross-talk is often compensated by the MIMO decoding algorithm, while the temporal cross talk is mitigated using an equalizer. However, the LEDs have a non-linear transfer function and the performance of linear equalizers are limited. In this letter, we propose a joint spatial and temporal equalization using an artificial neural network (ANN) for an MIMO-VLC system. We demonstrate using a practical imaging/non-imaging optical MIMO link that the ANN-based joint equalization outperforms the joint equalization using a traditional decision feedback as ANN is able to compensate the non-linear transfer function as well as cross talk.",

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Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System

Abstract

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Keywords

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