Nonlinearities compensator based on SORBFNN in ultra-long-haul high-capacity CO-OFDM systems

Abstract

Radial basis function neural network is recognized as being the most useful type of neural network for a nonlinear equalizer in the CO-OFDM system. In the history of nonlinear equalizers, the focus has always been on mitigation of nonlinearity. Despite this interest, no one to the best of our knowledge has studied ultra-long-haul transmission length. Previous work has been limited to normal transmission length, i.e., up to 1400 km. In ultra-long-haul optical fiber communication systems, the optical signal becomes very much complex at longer distance, i.e., around over 8000 km. It has not yet been established whether radial basis function neural network can do the same improvement in performance at ultra-long-haul transmission length. A basic issue of a customary radial basis function neural system is its low performance at such a long distance. This paper presents a new approach to compensating nonlinearities in ultra-long-haul optical fiber communication CO-OFDM systems. In this new approach, a radial basis function with nonlinear equalizer (first hidden layer with automatically adjusted structure, i.e., number of neuron nodes in the hidden layer and the different parameters of the structure through learning, and second hidden layer with fixed structure) based on two hidden layers has been recommended. The aim of this study is to examine the performance of a self-organizing radial basis function neural network-based nonlinear equalizer (SORBFNN-NLE) for the mitigation of nonlinearities in ultra-long-haul CO-OFDM systems. Better performance of the SORBFNN is attributed due to self-organizing structure and training of network. From this study, it has been observed that the proposed SORBFNN-NLE performs well regarding Q-factor and OSNR at an ultra-long distance.