Open AccessNon‐linearities mitigation with fuzzy neural networks using a machine learning algorithm in a CO‐OFDM system
Author(s) -
Kaur Gurpreet,
Kaur Gurmeet
Publication year - 2020
Publication title -
iet optoelectronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 42
eISSN - 1751-8776
pISSN - 1751-8768
DOI - 10.1049/iet-opt.2018.5116
Subject(s) - equaliser , orthogonal frequency division multiplexing , artificial neural network , computer science , fuzzy logic , algorithm , wilcoxon signed rank test , outlier , artificial intelligence , telecommunications , mathematics , decoding methods , statistics , channel (broadcasting) , mann–whitney u test
This study presents a fuzzy neural network based non‐linear equaliser to diminish the non‐linearities in a coherent optical orthogonal frequency division multiplexing (CO‐OFDM) system. The numerical results show that the proposed technique based CO‐OFDM system outperforms the CO‐OFDM system without non‐linear equaliser by 5 and 7.5% EVM performance, and 2.36 and 4.87 dB Q ‐factor performance after 1000 km transmission and −3 dBm input launch power at a bit rate of 40 and 80 Gbps, respectively. Moreover, it has been generally accepted in statistics that the rank‐based Wilcoxon methodology provide more robust results in a contradiction of outliers. Therefore, the aim of this study is to analyse fuzzy neural network based non‐linear equaliser and compare the results with that of Wilcoxon approach fuzzy neural network based non‐linear equaliser.