Feedforward and Recurrent Neural Network-Based Transfer Learning for Nonlinear Equalization in Short-Reach Optical Links

Abstract

Neural network (NN)-based nonlinear equalizers have been shown effective for various types of short-reach direct detection systems. However, they work best for a certain channel condition and need to be trained again when the channel environment is changed, which hinders the efficient deployment of future optical switched data center networks. In this article, we propose transfer learning (TL)-aided feedforward neural networks (FNN) and recurrent neural networks (RNN) for nonlinear equalization in short-reach direct detection optical links, which enables a fast transition to new equalizers when the channel condition is changed. A 50-Gb/s 20-km pulse amplitude modulation (PAM)-4 optical link ..