Probabilistic shaping and neural network-based optimization for a nonlinear frequency division multiplexing system
Jiacheng Wei, Lixia Xi, Xulun Zhang, Jiayun Deng, Shucheng Du, Xiaoguang Zhang, Wenbo Zhang, Xiaosheng Xiao
Abstract
A joint scheme introducing probabilistic shaping (PS) at the transmitter and utilizing a neural network (NN) equalizer at the receiver is proposed to improve the performance of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>b</mml:mi> </mml:math> -modulated nonlinear frequency division multiplexing (NFDM) system. Through a numerical simulation, we demonstrate that PS plays a leading role for low launch power case, which improves the performance of the system effectively, while the NN equalizer’s superiority appears in a high launch power region, whose main role is to weaken the correlation among subcarriers for improving system performance. The proposed scheme would enlighten the optimum modulation and detection schemes of the NFDM system.