Premium
Advanced nonlinear equalizer for Filter Bank Multicarrier‐based Long Reach‐Passive Optical Network system
Author(s) -
L Jerart Julus,
D Manimegalai,
C Asha Beaula,
J Joshan Athanesious,
A Andrew Roobert
Publication year - 2021
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4921
Subject(s) - computer science , equalizer , filter bank , nonlinear system , artificial neural network , adaptive equalizer , telecommunications link , filter (signal processing) , electronic engineering , channel (broadcasting) , signal (programming language) , telecommunications , artificial intelligence , engineering , physics , quantum mechanics , computer vision , programming language
Summary The performance of the intensity‐modulated Filter Bank Multicarrier (FBMC) system using direct detection with advanced nonlinear equalizer in Long Reach‐Passive Optical Network (LR‐PON) is presented in this paper. First, the performance of the FBMC system and its nonlinearity in the channel are analyzed. We introduce two nonlinear equalizers, namely, Artificial Neural Networks–Nonlinear Feed‐Forward Equalizer (ANN‐NFFE) and Deep Neural Network–Nonlinear Equalizer (DNN‐NLE). Both the equalizers can mitigate the nonlinearities in the signal. Also, the impact of the system in downlink is analyzed using simulation by varying the data rates for various fiber lengths. The FBMC using SMT provides better spectral efficiency. The performance of both equalizers is compared. The addition of the DNN‐NLE equalizer provides a better OSNR, good accuracy, and better BER compared with ANN‐NFFE.