基于CNN-BiLSTM-Attention模型的光纤非线性损伤补偿算法  

Fiber Nonlinear Impairments Compensation Algorithm Based on CNN-BiLSTM-Attention Model

作　　者：陈志轩 张洪波 张敏[1] 蔡炬[1] 刘娇 杜杰 张倩武[2] CHENG Zhixuan;ZHANG Hongbo;ZHANG Min;CAI Ju;LIU Jiao;DU Jie;ZHANG Qianwu(College of Communication Engineering,Chengdu University of Information Technology,Chengdu 610225,Sichuan Province,China;Key Laboratory of Specialty Fiber Optics and Optical Access Networks,Shanghai University,Baoshan District,Shanghai 200072,China)

机构地区：[1]成都信息工程大学通信工程学院,四川省成都市610225 [2]上海大学特种光纤与光接入网省部共建重点实验室,上海市宝山区200072

出　　处：《电力信息与通信技术》2023年第11期7-12,共6页Electric Power Information and Communication Technology

基　　金：四川省科技计划(2021YFG0149);上海市科委重点实验室项目(SKLSFO2019-06);高等学校学科创新引智计划(111)(D20031)。

摘　　要：克尔效应和色散对相干光纤通信系统的传输距离和数据容量有极大限制。为了补偿光纤传输中的非线性损伤,结合卷积神经网络(convolutional neural network,CNN)、双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)和注意力机制(attention)的特点,提出了一种基于CNN-BiLSTM-Attention模型的光纤非线性损伤补偿算法,并在DP-16QAM 30Gbaud的相干光通信系统中进行了仿真。仿真结果表明,与CNN-BiLSTM模型相比,在1200 km的传输距离下,该算法以降低0.03~0.23 dB的Q因子为代价,使复杂度降低了约31.6%;在相似复杂度下,该算法在最佳传输功率下的Q因子提高了0.43 dB。The transmission distance and data capacity of coherent optical fiber communication systems are greatly limited by the Kerr effect and chromatic dispersion.To compensate the nonlinear impairments in optical fiber transmission,combined with a convolutional neural network layer,a bi-directional long short-term memory layer and an attention layer,a fiber nonlinearity compensation algorithm based on the CNN-BiLSTM-Attention model is proposed and simulated in a DP-16QAM 30Gbaud coherent optical system.The simulation results demonstrate that,compared to the CNN-BiLSTM model,the proposed algorithm achieves a reduction of 31.6%in the number of real multiplications required to equalize per symbol,but at the cost of lowering the Q-factor by 0.03 dB to 0.23 dB when transmitting over a distance of 1200 km.Additionally,the Q-factor of the algorithm is improved by 0.43 dB at the optimal launch power under similar complexity.

关 键 词：光纤非线性损伤补偿 卷积神经网络 双向长短期记忆网络 注意力机制 

分 类 号：TN929.5[电子电信—通信与信息系统]