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

Rajbhandari, Sujan and Chun, Hyunchae and Faulkner, Graheme and Haas, Harald and Xie, Enyuan and McKendry, Jonathan J. D. and Herrnsdorf, Johannes and Gu, Erdan and Dawson, Martin D. and O'Brien, Dominic (2019) Neural network based joint spatial and temporal equalization for MIMO-VLC system. IEEE Photonics Technology Letters, 31 (11). pp. 821-824. ISSN 1041-1135

[thumbnail of Rajbhandari-etal-IEEE-PTL-2019-Neural-network-based-joint-spatial-and-temporal]
Text (Rajbhandari-etal-IEEE-PTL-2019-Neural-network-based-joint-spatial-and-temporal)
Accepted Author Manuscript

Download (302kB)| Preview


    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.

    ORCID iDs

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