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

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.