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

机译:采用MIMO深神经网络均衡器补偿相干光学OFDM系统中的非线性失真

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摘要

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 Q-factor and reducing by a factor of three the computational cost. (C) 2020 Optical Society of America
机译:提出了一种基于多输入多输出(MIMO)深神经网络(DNN)的新型非线性均衡器,并实验证明了40 GB / S相干光正交频分复用系统中子载波间非线性的补偿。 实验结果表明,与线性补偿或传统的单输入单输出DNN相比,MIMO-DNN在2000公里的标准单模光纤传输中将电源裕度延伸4 dB。 还发现MIMO-DNN通过增加高达1 dB的有效Q系数并减少三个计算成本来优于数字回传播。 (c)2020美国光学学会

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