{"title":"Data-Driven Erbium-Doped Fiber Amplifier Gain Modeling Using Gaussian Process Regression","authors":"Calum Harvey;Md. Saifuddin Faruk;Seb J. Savory","doi":"10.1109/LPT.2024.3441110","DOIUrl":null,"url":null,"abstract":"We propose a data-driven erbium-doped fiber amplifier (EDFA) gain model utilizing Gaussian process regression (GPR). An additive Laplacian and radial-basis function kernel is proposed for the GPR and was found to outperform deep neural network (DNN) methods while additionally providing prediction uncertainty. Performance is measured using mean absolute error (MAE) averaged across five different EDFAs with three manufacturers. The GPR achieves an MAE of 0.1 dB using 30 training samples in contrast to the DNN that achieves an MAE of 0.25 dB using 3000 training samples. Additionally, we demonstrate that active learning can be used to improve robustness and repeatability of convergence.","PeriodicalId":13065,"journal":{"name":"IEEE Photonics Technology Letters","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Photonics Technology Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10632168/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
We propose a data-driven erbium-doped fiber amplifier (EDFA) gain model utilizing Gaussian process regression (GPR). An additive Laplacian and radial-basis function kernel is proposed for the GPR and was found to outperform deep neural network (DNN) methods while additionally providing prediction uncertainty. Performance is measured using mean absolute error (MAE) averaged across five different EDFAs with three manufacturers. The GPR achieves an MAE of 0.1 dB using 30 training samples in contrast to the DNN that achieves an MAE of 0.25 dB using 3000 training samples. Additionally, we demonstrate that active learning can be used to improve robustness and repeatability of convergence.
期刊介绍:
IEEE Photonics Technology Letters addresses all aspects of the IEEE Photonics Society Constitutional Field of Interest with emphasis on photonic/lightwave components and applications, laser physics and systems and laser/electro-optics technology. Examples of subject areas for the above areas of concentration are integrated optic and optoelectronic devices, high-power laser arrays (e.g. diode, CO2), free electron lasers, solid, state lasers, laser materials'' interactions and femtosecond laser techniques. The letters journal publishes engineering, applied physics and physics oriented papers. Emphasis is on rapid publication of timely manuscripts. A goal is to provide a focal point of quality engineering-oriented papers in the electro-optics field not found in other rapid-publication journals.