Radial basis function neural network enabled C-band 4 × 50  Gb/s PAM-4 transmission over 80  km SSMF

We propose a novel radial basis function neural network (RBF-NN)-based nonlinear equalizer (NLE) for the intensity modulation/direct detection (IM/DD) transmission. After optimizing input characteristics of the RBF-NN, we experimentally demonstrate C-band 4×50  Gb/s four-level pulse-amplitude modulation (PAM-4) transmission over 80 km standard single-mode fiber (SSMF), using 18 GHz direct-modulated lasers and dispersion compensation fiber. We demonstrate that the RBF-NN-based NLE outperforms the commonly used Volterra filter equalizer and recently proposed multilayer perceptron (MLP)-based NLE by about 4.5 and 1.5 dB improvements of receiver sensitivity at the 7% forward error correction threshold, respectively. Furthermore, we identify that both the training stability and fitting ability of the RBF-NN-based NLE are better than those of the MLP-based NLE.