Neural networks for modelling nonlinear pulse propagation

The interaction of dispersion and nonlinear effects gives rise to a wide variety of pulse dynamics and proves to be a fundamental bottleneck for high speed communications. Traditionally, time consuming and computationally inefficient algorithms are used for this purpose1 and therefore, research in nonlinear optics and optical communications is now implementing machine learning based methods.2–5 We show a comprehensive comparison of different neural network (NN) architectures to learn the nonlinear Schrodinger equation (NLSE). We have used a NN based approach to reconstruct the pulse (temporal and spectral domain) at the transmitter from the pulse received through a highly nonlinear fiber (HNLF) without the prior knowledge of fiber parameters. Additionally, the trained network can also predict the dispersion and nonlinear parameters of an unknown fiber. The proposed NN also mitigates the need of using iterative reconstruction methods which are computationally expensive and slow. A detailed comparison of six different NN based techniques namely fully connected NN (FCNN), cascade NN (CaNN), convolutional NN (CNN), long short term memory networks (LSTM), bidirectional LSTM (BiLSTM) and gated recurrent unit (GRU) is presented. To our knowledge, the literature does not contain a detailed discussion of the NN architecture which is most suitable for learning the transfer function of the fiber. We perform a comprehensive study by including all popular NN architectures which enables the estimation of pulse profile for arbitrary pulse width, chirp, second and third order dispersion, nonlinearity and fiber length which can benefit nonlinear optics experiments and coherent optical communications. The growing popularity of NNs is resulting in increased design and development of hardware that is optimized for processing NN architectures. In light of this flexibility and optimised hardware, popularity of NN in optics is set to increase.