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
SN - 1041-1135
VL - 31
SP - 821
EP - 824
JO - IEEE Photonics Technology Letters
JF - IEEE Photonics Technology Letters
IS - 11
ER -