基于分段前馈神经网络的短距离DML-DD系统非线性均衡器

2021 19th International Conference on Optical Communications and Networks (ICOCN) Pub Date : 2021-08-23 DOI:10.1109/ICOCN53177.2021.9563682

Qi Wu, Yixiao Zhu, Weisheng Hu

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引用次数: 2

摘要

针对直接调制激光(DML)光强调制直接检测(IM-DD)系统中存在的线性和非线性畸变，提出了一种有针对性的分段前馈神经网络(PW-FNN)。通过该均衡器，实验证明了在30km标准单模光纤(SSMF)上使用16.8GHz DML传输56Gbit/s脉冲幅度调制(PAM)-4，误码率(BER)低于7%硬判决前向纠错(HD-FEC)阈值3.8 × 10−3。与传统的FNN均衡器和voltera相比，PW-FNN均衡器的接收机灵敏度提高了2dB以上。实验结果验证了所提出的PW-FNN是一种很有前途的解决短距离dml - IM-DD系统的方法。

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Piecewise Feedforward Neural Network Based Nonlinear Equalizer for Short-Reach DML-DD System

We propose a targeted piecewise feedforward neural network (PW-FNN) to mitigate both the linear and nonlinear distortions in intensity modulation direct detection (IM-DD) system based on direct modulated laser (DML). By this equalizer, a 56Gbit/s pulse amplitude modulation (PAM)-4 transmission over 30km standard single-mode fiber (SSMF) is experimentally demonstrated with a 16.8GHz DML with bit error rate (BER) below 7% hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10−3. Compared with Volttera and traditional FNN equalizer, the proposed PW-FNN equalizer exhibits more than 2dB receiver sensitivity improvement. The experimental results validate that the proposed PW-FNN can be a promising solution for short reach DML-based IM-DD system.

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2021 19th International Conference on Optical Communications and Networks (ICOCN)

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