Open AccessA novel support vector machine robust model based electrical equaliser for coherent optical orthogonal frequency division multiplexing systems
Author(s) -
Mhatli Sofien,
Mrabet Hichem,
Dayoub Iyad,
Giacoumidis Elias
Publication year - 2017
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2016.1115
Subject(s) - equaliser , bit error rate , orthogonal frequency division multiplexing , support vector machine , electronic engineering , computer science , algorithm , control theory (sociology) , mathematics , telecommunications , artificial intelligence , engineering , channel (broadcasting) , decoding methods , control (management)
Classifiers, such as artificial neural networks non‐linear equaliser (ANN‐NLE), Wiener–Hammerstein non‐linear equaliser, Volterra non‐linear equaliser (Volterra‐NLE) and support vector machine non‐linear equaliser (SVM‐NLE), can play a significant role in compensating non‐linear imperfections in the optical communications context. Using classifiers to mitigate the non‐linear effects in coherent optical orthogonal frequency division multiplexing (CO‐OFDM) systems is an interesting idea to be investigated. In this study, a novel support vector machine robust version, specifically adapted to a 100 Gb/s CO‐OFDM data structure for long haul distance, is proposed. Firstly, the authors demonstrate that SVM‐NLE upgrades the system performance by about 10 −1 in terms of bit‐error rate compared to Volterra‐NLE at optical signal‐to‐noise ratio equal to 14 dB. Then, they show that it can double the transmission distance up to 1600 km over single mode fibre channel. Furthermore, a performance comparison is performed using 16 quadrature amplitude modulation and 40 Gb/s bit rate for SVM‐NLE, ANN‐NLE and inverse Volterra series transfer function non‐linear equaliser, respectively.