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

Sujan Rajbhandari, Hyunchae Chun, Graheme 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)
47 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
Pages (from-to)821-824
Number of pages4
JournalIEEE Photonics Technology Letters
Issue number11
Early online date4 Apr 2019
Publication statusPublished - 1 Jun 2019


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


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