Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System
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 suff...
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| Veröffentlicht in: | IEEE photonics technology letters 2019-06, Vol.31 (11), p.821-824 |
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| container_title | IEEE photonics technology letters |
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| creator | Rajbhandari, Sujan Hyunchae Chun Faulkner, Grahame Haas, Harald Enyuan Xie McKendry, Jonathan J. D. Herrnsdorf, Johannes Gu, Erdan Dawson, Martin D. O'Brien, Dominic |
| description | 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. |
| doi_str_mv | 10.1109/LPT.2019.2909139 |
| format | Article |
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D. ; Herrnsdorf, Johannes ; Gu, Erdan ; Dawson, Martin D. ; O'Brien, Dominic</creator><creatorcontrib>Rajbhandari, Sujan ; Hyunchae Chun ; Faulkner, Grahame ; Haas, Harald ; Enyuan Xie ; McKendry, Jonathan J. D. ; Herrnsdorf, Johannes ; Gu, Erdan ; Dawson, Martin D. ; O'Brien, Dominic</creatorcontrib><description>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. 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In this letter, we propose a joint spatial and temporal equalization using an artificial neural network (ANN) for an MIMO-VLC system. 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| subjects | Algorithms artificial neural network Artificial neural networks Communications systems Crosstalk Decision feedback equalizers Decoding Equalization Equalizers joint equalization Light emitting diodes MIMO (control systems) MIMO communication multiple input multiple output Neural networks non-linear transfer function Optical communication Receivers Transfer functions Visible light communications White light |
| title | Neural Network-Based Joint Spatial and Temporal Equalization for MIMO-VLC System |
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