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

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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> , 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.