Meta-Learning Assisted Source Domain Optimization for Transfer Learning Based Optical Fiber Nonlinear Equalization

Abstract

Transfer learning (TL) has been demonstrated its feasibility on fast remodeling for fiber nonlinearity equalization. It will be very efficient with fine-tuning rather than retraining when the channel parameters have been changed, e.g., the optical launch power and fiber distance. However, the performance is dependent on the highly correlation between the source and target tasks. An improper source domain cannot improve the efficiency or even negative transfer. In this paper, we propose a meta-learning assisted source domain optimization scheme in single- and multi-channel transmissions. We construct the support dataset and query dataset from the information of the inner-task and the across-task to train the neural network so that we can find a unified source domain and efficiently adapt to different tasks with fewer epochs. We conduct an experiment to transmit 120-Gb/s dual polarization (DP) 16-QAM signal over 800-km standard single mode fiber (SSMF). The required epochs for convergence can be significantly reduced and there is slight Q-factor improvement with meta-learning algorithm compared with retraining and conventional TL. We also analyze the TL strategy in multi-channel system. The simulation results show that meta-learning can also help to optimize the source domain with lower complexity.