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TL-ANN Based Nonlinear Equalization for Multi-Users Radio Over Fiber System

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.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol. 41, No. 5, March 1 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1399-1405

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

TRANSFER LEARNING
ARTIFICIAL NEURAL NETWORKS
RADIO OVER FIBER
NONLINEAR EQUALIZATION

Info Detail Spesifik

DOI: 10.1109/JLT.2022.3221166

Pernyataan Tanggungjawab

Enji Liu, Zhenming Yu, Yunyu Song, Kaixuan Sun, Hongyu Huang, Feifei Yin, ... (et.al.)

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