Performance Assessment of Joint Optical-Digital Nonlinearity Mitigation Schemes in Long-Haul Systems

The combined approach of optical and digital nonlinearity mitigation techniques have been shown to have an edge over the individual schemes, tackling their drawbacks and improving compensation capabilities. We demonstrated fiber-nonlinearity mitigation of 32 GBd single-polarization 16QAM signals transmitted over an 800-km dispersion-managed link using optical phase conjugation (OPC) and digital-domain neural networks (NN). Our implemented NN comprised a recurrent neural network (RNN), and a reservoir computing network (RCN). To further compensate for the penalty introduced by the design of OPC, we employed NN schemes in the digital signal processing and applied it to the signal after transmission in the OPC-assisted link. We present the results for optimizing the NN models for important hyper-parameters like the number of hidden layer neurons and the input vector size. The proposed joint approach achieves Q2-factor improvements up to 1.8 dB while surpassing the improvements of the individual schemes. Our experiments indicate that the joint approach has the potential to reduce the overall complexity of NN architectures in terms of the size of the hidden layer and input vector.