Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation

Abstract

Citation: Rajbhandari, Sujan, Ghassemlooy, Zabih and Angelova, Maia (2009) Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation. IET Optoelectronics, 3 (4 Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.)The AbstractThe bit error rate (BER) performance of a pulse position modulation (PPM) scheme for non line-of-sight (LOS) indoor optical links employing channel equalization based on the artificial neural network (ANN) is reported in the paper. Channel equalization is achieved by training a multilayer perceptrons (MLP) ANN. A comparative study of the unequalized "soft" decision decoding and the "hard" decision decoding along with the neural equalized "soft" decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalized "hard" decision decoding performs the worst for all values of normalized delayed spread becoming impractical beyond normalized delayed spread of 0.6. However, "soft" decision decoding with/without equalization displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel the signal-to-noise ratio (SNR) requirement to achieve a BER of 10 -5 for the ANN based equalizer is ~ 10 dB lower compared with the unequalized "soft" decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalization is an effective tool of mitigating the ISI.

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