Evolving fuzzy neural network equalization of channel impulse response in optical mode division multiplexing

Anahed M. Kareem; Angela Amphawan

doi:10.25212/lfu.qzj.2.2.28

Authors

Anahed M. Kareem Scientific Information and Technology Transfer Center, Ministry of Science & Technology, Baghdad, Iraq
Angela Amphawan Head of Optical Computing & Technology Research Laboratory, School of Computing, Universiti Utara Malaysia

DOI:

https://doi.org/10.25212/lfu.qzj.2.2.28

Keywords:

evolving fuzzy neural network (EFUNN), mode division multiplexing (MDM), multimode Fiber (MMF), intersymbol interference (ISI).

Abstract

Optical mode division multiplexing (MDM) systems suffer from the inter-symbol interference (ISI) issues due to nonlinear channel impairments in multimode fiber from mode coupling and modal dispersion. Existing equalization algorithms in MDM systems such as least mean square (LMS) and recursive least squares (RLS) operate linearly and thus far, are unable to effectively mitigate the nonlinear channel impairments. To address this issue, a nonlinear evolving fuzzy neural network (EFuNN) equalization scheme was developed in MATLAB to reshape the channel impulse response of a MDM system comprising five Laguerre-Gaussian (LG) modes at the transmitter to five reference Gaussian pulses at separate time intervals at the receiver in order to mitigate the effects of ISI from nonlinear channel impairments.

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