Nonlinear Impairment Compensation Using Transfer Learning-Assisted Convolutional Bidirectional Long Short-Term Memory Neural Network for Coherent Optical Communication Systems

By combining the nonlinear impairment features derived from the first-order perturbation theory, we propose a nonlinear impairment compensation (NLC) scheme based on the transfer learning-assisted convolutional bidirectional long short-term Memory (CNN-BiLSTM) neural network structure. When considering the correlation of nonlinear impairment between preceding and succeeding consecutive adjacent symbols on the current moment symbol and integrating the multidimensional feature extraction and time memory characteristics of CNN-BiLSTM, the nonlinear impairment information contained in the input feature can be fully utilized to accurately predict the nonlinear impairment showing significant compensation effect. Meanwhile, transfer learning (TL) is introduced to greatly reduce the complexity of the scheme on the basis of high compensation performance. To verify the effectiveness of the proposed scheme, we construct single-channel (SC) and 5-channel 28 GBaud polarization division multiplexing 16 quadrature amplitude modulation (PDM-16QAM)/85 GBaud PDM-64QAM simulation systems, and SC and 3-channel 28 GBaud PDM-16QAM experimental systems. The experimental results show that when compared with simple recurrent neural network (SRNN) NLC and DBP 20 steps per span (DBP20StPs), the Q-factor gain of our scheme is about 1 dB and 1.7 dB in the SC system, and about 1.1 dB and 1.5 dB in the 3-channel system at the optimal launch power, respectively. It is interesting to highlight that, by applying TL to the simulation and experimental systems, our scheme based on only 5% of the training samples can achieve compensation performance comparable to or higher quality than retraining at various launch powers.