Neural network based joint spatial and temporal equalization for MIMO-VLC system

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

Research output: Contribution to journal › Article › peer-review

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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
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

Keywords

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

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10.1109/LPT.2019.2909139

Rajbhandari-etal-IEEE-PTL-2019-Neural-network-based-joint-spatial-and-temporalAccepted author manuscript, 295 KB

Ultra-Parallel Visible Light Communications (UP-VLC)
Dawson, M. (Principal Investigator), Calvez, S. (Co-investigator) & Watson, I. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/10/12 → 28/02/17
Project: Research

Cite this

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title = "Neural network based joint spatial and temporal equalization for MIMO-VLC system",

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.",

keywords = "visible light communications, multiple input multiple output, MIMO, joint equalization, artificial neural network, non-linear transfer function",

author = "Sujan Rajbhandari and Hyunchae Chun and Graheme Faulkner and Harald Haas and Enyuan Xie and McKendry, {Jonathan J. D.} and Johannes Herrnsdorf and Erdan Gu and Dawson, {Martin D.} and Dominic O'Brien",

note = "{\textcopyright} 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.",

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AU - Rajbhandari, Sujan

AU - Chun, Hyunchae

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AU - Xie, Enyuan

AU - McKendry, Jonathan J. D.

AU - Herrnsdorf, Johannes

AU - Gu, Erdan

AU - Dawson, Martin D.

AU - O'Brien, Dominic

N1 - © 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.

PY - 2019/6/1

Y1 - 2019/6/1

N2 - 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.

AB - 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.

KW - visible light communications

KW - multiple input multiple output

KW - MIMO

KW - joint equalization

KW - artificial neural network

KW - non-linear transfer function

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DO - 10.1109/LPT.2019.2909139

M3 - Article

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VL - 31

SP - 821

EP - 824

JO - IEEE Photonics Technology Letters

JF - IEEE Photonics Technology Letters

IS - 11

ER -

Neural network based joint spatial and temporal equalization for MIMO-VLC system

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Ultra-Parallel Visible Light Communications (UP-VLC)

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