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使用基于多输入多输出深度神经网络的均衡器对相干光正交频分复用系统中的非线性失真进行补偿。

Compensation of nonlinear distortion in coherent optical OFDM systems using a MIMO deep neural network-based equalizer.

作者信息

Aldaya Ivan, Giacoumidis Elias, Tsokanos Athanasios, Jarajreh Mutsam, Wen Yannuo, Wei Jinlong, Campuzano Gabriel, Abbade Marcelo L F, Barry Liam P

出版信息

Opt Lett. 2020 Oct 15;45(20):5820-5823. doi: 10.1364/OL.403778.

Abstract

A novel nonlinear equalizer based on a multiple-input multiple-output (MIMO) deep neural network (DNN) is proposed and experimentally demonstrated for compensation of inter-subcarrier nonlinearities in a 40 Gb/s coherent optical orthogonal frequency division multiplexing system. Experimental results reveal that MIMO-DNN can extend the power margin by 4 dB at 2000 km of standard single-mode fiber transmission when compared to linear compensation or conventional single-input single-output DNN. It is also found that MIMO-DNN outperforms digital back propagation by increasing up to 1 dB the effective-factor and reducing by a factor of three the computational cost.

摘要

提出了一种基于多输入多输出(MIMO)深度神经网络(DNN)的新型非线性均衡器,并通过实验证明其可用于补偿40 Gb/s相干光正交频分复用系统中的子载波间非线性。实验结果表明,与线性补偿或传统单输入单输出DNN相比,MIMO-DNN在2000 km标准单模光纤传输时可将功率裕度扩展4 dB。还发现,MIMO-DNN通过将有效因子提高多达1 dB并将计算成本降低三分之一,优于数字反向传播。

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