{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/machine-learning-based-edfa-gain-model","title":"Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices","arxiv_id":"2009.05326","date":"2020-09-11","proceeding":null,"authors":["Francesco Da Ros","Uiara Celine de Moura","Metodi P. Yankov"],"abstract":"We report a neural-network based erbium-doped fiber amplifier (EDFA) gain model built from experimental measurements. The model shows low gain-prediction error for both the same device used for training (MSE $\\leq$ 0.04 dB$^2$) and different physical units of the same make (generalization MSE $\\leq$ 0.06 dB$^2$).","url_abs":"https://arxiv.org/abs/2009.05326v1","url_pdf":"https://arxiv.org/pdf/2009.05326v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"machine-learning-based-edfa-gain-model","repo_url":"https://github.com/myankov/EDFA-data-reading-scripts","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}