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

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

    Research output: Contribution to journalArticlepeer-review

    27 Citations (Scopus)
    127 Downloads (Pure)


    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 languageEnglish
    Article number8681630
    Pages (from-to) 821 - 824
    Number of pages4
    JournalIEEE Photonics Technology Letters
    Issue number11
    Early online date4 Apr 2019
    Publication statusPublished - 1 Jun 2019

    Bibliographical note

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    • artificial neural network
    • joint equalization
    • multiple input multiple output
    • non-linear transfer function
    • Visible light communications

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Atomic and Molecular Physics, and Optics
    • Electrical and Electronic Engineering


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