Nonlinear Equalization Based on Artificial Neural Network in DML-Based OFDM Transmission Systems

被引：0

作者：

Huang, Wei-Hsiang ^{[1
]}

Nguyen, Hong-Minh ^{[2
]}

Wang, Chung-Wen ^{[1
]}

Chan, Min-Chi ^{[1
]}

Wei, Chia-Chien ^{[1
]}

Chen, Jyehong ^{[2
]}

Taga, Hidenori ^{[3
]}

Tsuritani, Takehiro ^{[4
]}

机构：

[1] Department of Photonics, National Sun Yat-sen University, Kaohsiung, Taiwan

[2] Department of Photonics, National Chiao Tung University, Hsinchu, Taiwan

[3] Photonic Transport Network Laboratory, KDDI Research Inc., Saitama, Japan

[4] Future Network Infrastructure Division 1, KDDI Research Inc., Saitama, Japan

来源：

Journal of Lightwave Technology | 2021年 / 39卷 / 01期

关键词：

Neural networks - Cost functions - Light transmission - Equalizers - Nonlinear optics - Modulation - Signal to noise ratio;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

This article reports the application of an equalizer based on an artificial neural network (ANN), in the form of nonlinear waveform regression, to mitigate nonlinear impairments in directly modulated laser (DML)-based orthogonal frequency-division multiplexing (OFDM) optical transmission. Experiments involving transmission over 0-200 km demonstrate that using an ANN with one hidden layer can greatly reduce nonlinear distortion. The proposed scheme outperformed a Volterra nonlinear equalizer at transmission distances exceeding 25 km. Using a 10G-class DML, the proposed scheme achieved the following data rates: 39.2 Gbps at 100 km (an improvement of 59%) and 33.5 Gbps at 150 km (an improvement of 57%). We also modified the cost function of the ANN during the training procedure to overcome the poor signal-to-noise ratio of the original ANN at low frequencies. This resulted in $>$30-Gbps transmission over 0-200 km. © 1983-2012 IEEE.

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页码：73 / 82

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