Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM

被引：9

作者：

Giacoumidis, E. ^{[1
]}

Tsokanos, A. ^{[2
]}

Ghanbarisabagh, M. ^{[3
]}

Mhatli, S. ^{[4
]}

Barry, L. P. ^{[1
]}

机构：

[1] Dublin City Univ, Dublin 9, Ireland

[2] Univ Hertfordshire, Hatfield AL10 9AB, Herts, England

[3] Islamic Azad Univ, Fac Elect Engn & Comp Sci, Dept Elect Engn, North Tehran Branch, Tehran 1651153311, Iran

[4] Carthage Univ, Tunisia Polytech Sch, SERCom Lab, La Marsa 2078, Tunisia

来源：

IEEE PHOTONICS TECHNOLOGY LETTERS | 2018年 / 30卷 / 12期

基金：

爱尔兰科学基金会; 欧盟地平线“2020”;

关键词：

Optical OFDM; optical fiber communication; machine learning; fiber nonlinearity compensation;

D O I：

10.1109/LPT.2018.2832617

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A novel blind nonlinear equalization (BNLE) technique based on the iterative re-weighted least square is experimentally demonstrated for single-and multi-channel coherent optical orthogonal frequency-division multiplexing. The adopted BNLE combines, for the first time, a support vector machine-learning cost function with the classical Sato or Godard error functions and maximum likelihood recursive least-squares. At optimum launched optical power, BNLE reduces the fiber nonlinearity penalty by similar to 1 (16-QAM single-channel at 2000 km) and similar to 1.7 dB (QPSK multi-channel at 3200 km) compared to a Volterra-based NLE. The proposed BNLE is more effective for multi-channel configuration: 1) it outperforms the "gold-standard" digital-back propagation and 2) for a high number of subcarriers the performance is better due to its capability of tackling inter-subcarrier four-wave mixing.

引用

页码：1091 / 1094

页数：4

共 11 条

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[Anonymous], P OPT FIB COMM C EXH

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