Abstract
To mitigate nonlinear distortions in multi-user analog radio over fiber (A-RoF) system, a nonlinear equalizer (NLE) which combines transfer learning (TL) and waveform regression-based artificial neural network (ANN) is proposed and validated experimentally in this research. Unlike traditional ANNs, waveform regression ANNs can be regarded as waveform training ANNs and avoid complex-value training. In this paper, we first analyze the modulation distortions that appear in multi-user RoF systems and then propose a waveform regression ANN-based nonlinear equalizer (ANN-NLE). Subsequently, we use TL to reduce training costs and increase the compatibility of the ANN-NLE. Finally, an A-RoF system with multicore fiber is adopted to verify the proposed approach. According to the simulation and experimental results, the bit error ratio (BER) of a multi-user signal is decreased to the threshold of 3.8 × 10−3, and the improved optical receiver sensitivity is greater than 1.5 dB. This proposed method could realize nonlinear equalization and display good compatibility with different systems.
PDF Article
More Like This
Fiber nonlinearity-induced penalty reduction in CO-OFDM by ANN-based nonlinear equalization
Elias Giacoumidis, Son T. Le, Mohammad Ghanbarisabagh, Mary McCarthy, Ivan Aldaya, Sofien Mhatli, Mutsam A. Jarajreh, Paul A. Haigh, Nick J. Doran, Andrew D. Ellis, and Benjamin J. Eggleton
Opt. Lett. 40(21) 5113-5116 (2015)
Nonlinearity mitigation in a fiber-wireless integrated system based on low-complexity autoencoder and BiLSTM-ANN equalizer
Xiang Liu, Jiao Zhang, Min Zhu, Weidong Tong, Zhigang Xin, Yunwu Wang, Mingzheng Lei, Bingchang Hua, Yuancheng Cai, Yucong Zou, and Jianjun Yu
Opt. Express 31(12) 20005-20018 (2023)
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription