Artificial Neural Network Assisted Probabilistic and Geometric Shaping for Flexible Rate High-Speed PONs

In this paper, we employ artificial neural networks (ANNs) to optimize joint probabilistic shaping (PS) and geometric shaping (GS) for a realistic 50G IM/DD passive optical network (PON) link. Apart from being able to find a generalized mutual information (GMI)-maximizing modulation for channel conditions unseen at the training phase, the compatibility of the ANN training with Monte Carlo simulation also enables us to use a more complicated channel model that more closely resembles a real PON system where fiber dispersion, bandwidth limitation and digital signal processing (DSP) are present. The forward error correction (FEC) requirement that must be satisfied in an actual implementation is imposed on the learned modulation by including a normalized GMI (NGMI) penalty term in the loss function. The proposed scheme is demonstrated with simulations. Results show that the ANN can achieve similar performance compared to a case-by-case optimization while also being capable of generalizing to a wide range of received optical power (ROP) from −30 dBm to −18 dBm and/or a broad range of fiber distance from 0 km to 20 km. About 0.1-bits/symbol GMI improvement is attained compared to uniform modulation.