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

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Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System. / Rajbhandari, Sujan; Chun, Hyunchae; Faulkner, Grahame et al.
Yn: IEEE Photonics Technology Letters, Cyfrol 31, Rhif 11, 01.06.2019, t. 821 - 824.

Allbwn ymchwil: Cyfraniad at gyfnodolyn › Erthygl › adolygiad gan gymheiriaid

HarvardHarvard

Rajbhandari, S, Chun, H, Faulkner, G, Haas, H, Xie, E, McKendry, JJD, Herrnsdorf, J, Gu, E, Dawson, MD & O’Brien, D 2019, 'Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System', IEEE Photonics Technology Letters, cyfrol. 31, rhif 11, tt. 821 - 824. https://doi.org/10.1109/LPT.2019.2909139

APA

Rajbhandari, S., Chun, H., Faulkner, G., Haas, H., Xie, E., McKendry, J. J. D., Herrnsdorf, J., Gu, E., Dawson, M. D., & O’Brien, D. (2019). Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System. IEEE Photonics Technology Letters, 31(11), 821 - 824. https://doi.org/10.1109/LPT.2019.2909139

CBE

Rajbhandari S, Chun H, Faulkner G, Haas H, Xie E, McKendry JJD, Herrnsdorf J, Gu E, Dawson MD, O’Brien D. 2019. Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System. IEEE Photonics Technology Letters. 31(11):821 - 824. https://doi.org/10.1109/LPT.2019.2909139

MLA

Rajbhandari, Sujan et al. "Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System". IEEE Photonics Technology Letters. 2019, 31(11). 821 - 824. https://doi.org/10.1109/LPT.2019.2909139

VancouverVancouver

Rajbhandari S, Chun H, Faulkner G, Haas H, Xie E, McKendry JJD et al. Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System. IEEE Photonics Technology Letters. 2019 Meh 1;31(11):821 - 824. doi: 10.1109/LPT.2019.2909139

Author

Rajbhandari, Sujan ; Chun, Hyunchae ; Faulkner, Grahame et al. / Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System. Yn: IEEE Photonics Technology Letters. 2019 ; Cyfrol 31, Rhif 11. tt. 821 - 824.

RIS

TY - JOUR

T1 - Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System

AU - Rajbhandari, Sujan

AU - Chun, Hyunchae

AU - Faulkner, Grahame

AU - Haas, Harald

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 - artificial neural network

KW - joint equalization

KW - multiple input multiple output

KW - non-linear transfer function

KW - Visible light communications

U2 - 10.1109/LPT.2019.2909139

DO - 10.1109/LPT.2019.2909139

M3 - Article

VL - 31

SP - 821

EP - 824

JO - IEEE Photonics Technology Letters

JF - IEEE Photonics Technology Letters

SN - 1041-1135

IS - 11

ER -