Abstract
Distributed Raman amplifier (DRA) has been widely studied in recent decades because of its low noise figure and flexible gain. In this paper, we present a novel scheme of DRA with broadband amplified spontaneous emission(ASE) source as pump instead of discrete pump lasers. The broadband pump is optimized by machine learning based inverse design and shaped by programmable waveshaper, so as to realize the ultrafine, dynamic and arbitrary gain spectrum shaping of Raman amplification. For the target of flat gain spectrum, the maximum gain flatness of 0.1086 dB is realized based on the simulation results. For the target of arbitrary gain spectrum, we demonstrate four gain profiles with maximum root mean square error (RMSE) of 0.074 dB. To further measure the performance of arbitrary gain spectrum optimization, the probability density functions (PDF) of RMSE and Errormax are presented. Meanwhile, the numeral relationship between the bands of broadband pump and signal is also explored. Furthermore, this work has great application potential to compensate the gain distortion or dynamic change caused by other devices in communication systems.
Highlights
Distributed Raman amplifier (DRA) is an important amplification scheme for optical communication systems due to its low noise figure (NF) and wideband flexible gain [1,2]
In order to validate the universal reliability of this inverse design, the targets of gain spectra out of training set are input into neural networks (NN) and the attained pump spectra need go through the numerical simulation of configuration to calculate actual gain spectrum, which will be compared with target gain spectrum to measure the accuracy of NN
We demonstrate a novel distribute Raman amplifier with broadband pump to realize the ultrafine and arbitrary gain spectra
Summary
Distributed Raman amplifier (DRA) is an important amplification scheme for optical communication systems due to its low noise figure (NF) and wideband flexible gain [1,2]. Recent publication proposed a scheme of inverse NN, in which they utilized regression NN to predict flat and titled gain profile of DRAs. the performance of this NN might not meet an acceptable expection, a fine-tuning phase is applied to further optimization [23]. The optimization results show promising improvement on backward pumping DRAs over C and C+L band, achieving maximum error below 0.5 dB for C band and 1 dB for C+L band This scheme requires training for two NNs and iterations of gradient-descent algorithm, which would be quite time-consuming. In order to validate the universal reliability of this inverse design, the targets of gain spectra out of training set are input into NN and the attained pump spectra need go through the numerical simulation of configuration to calculate actual gain spectrum, which will be compared with target gain spectrum to measure the accuracy of NN
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