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High-speed PAM4 transmission with a GeSi electro-absorption modulator and dual-path neural-network-based equalization
Optics Letters ( IF 3.6 ) Pub Date : 2020-09-21 , DOI: 10.1364/ol.401242
Xiaoke Ruan , Fan Yang , Lei Zhang , Hao Ming , Yanping Li , Fan Zhang

Equalization based on artificial neural networks (NN) has proved to be an effective way for nonlinearity mitigation in various kinds of optical communication systems. In this Letter, we propose a novel methodology of dual-path neural network (DP-NN)-based equalization. By combining a linear equalizer with an input-pruned NN equalizer, DP-NN can effectively reduce the computation cost compared to a conventional NN equalizer. We confirm its feasibility through 4-ary pulse amplitude modulation (PAM4) transmission at a gross(net) bitrate of 160 Gb/s (133.3 Gb/s), based on a GeSi electro-absorption modulator operating at C-band. After a 2 km transmission, the bit error rate is below the 20% hard-decision forward-error-correction threshold of ${1.5} \times {{10}^{- 2}}$ with the DP-NN equalization, which outperforms the Volterra equalization and is comparable to conventional NN-based equalization.

中文翻译:

具有GeSi电吸收调制器和基于双路径神经网络的均衡的高速PAM4传输

基于人工神经网络(NN)的均衡已被证明是减轻各种光通信系统中非线性的一种有效方法。在这封信中,我们提出了一种基于双路径神经网络(DP-NN)均衡的新颖方法。通过将线性均衡器与输入修剪的NN均衡器相结合,与传统的NN均衡器相比,DP-NN可以有效降低计算成本。我们基于在C波段工作的GeSi电吸收调制器,通过以160 Gb / s(133.3 Gb / s)的总(净)比特率传输4进制脉冲幅度调制(PAM4)来证实其可行性。传输2公里后,误码率低于20%硬判决前向纠错阈值$ {1.5} \ times {{10} ^ {-2}} $ DP-NN均衡的性能优于Volterra均衡,与传统的基于NN的均衡相当。
更新日期:2020-10-02
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