Data rate enhancement in optical camera communications using an artificial neural network equaliser

Othman Isam Younus, Navid Bani Hassan, Zabih Ghassemlooy, Paul Anthony Haigh, Stanislav Zvanovec, Luis Nero Alves, Hoa Le Minh

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In optical camera communication (OCC) systems leverage on the use of commercial off-the-shelf image sensors to perceive the spatial and temporal variation of light intensity to enable data transmission. However, the transmission data rate is mainly limited by the exposure time and the frame rate of the camera. In addition, the camera's sampling will introduce intersymbol interference (ISI), which will degrade the system performance. In this paper, an artificial neural network (ANN)-based equaliser with the adaptive algorithm is employed for the first time in the field of OCC to mitigate ISI and therefore increase the data rate. Unlike other communication systems, training of the ANN network in OCC is done only once in a lifetime for a range of different exposure time and the network can be stored with a look-up table. The proposed system is theoretically investigated and experimentally evaluated. The results record the highest bit rate for OCC using a single LED source and the Manchester line code (MLC) non-return to zero (NRZ) encoded signal. It also demonstrates 2 to 9 times improved bandwidth depending on the exposure times where the system's bit error rate is below the forward error correction limit.

Original languageEnglish
Pages (from-to)42656-42665
Number of pages10
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 28 Feb 2020

Keywords

  • ANN equaliser
  • optical camera communication
  • rolling shutter
  • visible light communications

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  • Cite this

    Younus, O. I., Bani Hassan, N., Ghassemlooy, Z., Haigh, P. A., Zvanovec, S., Alves, L. N., & Minh, H. L. (2020). Data rate enhancement in optical camera communications using an artificial neural network equaliser. IEEE Access, 8, 42656-42665. https://doi.org/10.1109/ACCESS.2020.2976537