{"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/geometric-constellation-shaping-for-fiber","title":"Geometric Constellation Shaping for Fiber Optic Communication Systems via End-to-end Learning","arxiv_id":"1810.00774","date":"2018-10-01","proceeding":null,"authors":["Rasmus T. Jones","Tobias A. Eriksson","Metodi P. Yankov","Benjamin J. Puttnam","Georg Rademacher","Ruben S. Luis","Darko Zibar"],"abstract":"In this paper, an unsupervised machine learning method for geometric\nconstellation shaping is investigated. By embedding a differentiable fiber\nchannel model within two neural networks, the learning algorithm is optimizing\nfor a geometric constellation shape. The learned constellations yield improved\nperformance to state-of-the-art geometrically shaped constellations, and\ninclude an implicit trade-off between amplification noise and nonlinear\neffects. Further, the method allows joint optimization of system parameters,\nsuch as the optimal launch power, simultaneously with the constellation shape.\nAn experimental demonstration validates the findings. Improved performances are\nreported, up to 0.13 bit/4D in simulation and experimentally up to 0.12 bit/4D.","url_abs":"http://arxiv.org/abs/1810.00774v1","url_pdf":"http://arxiv.org/pdf/1810.00774v1.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":"geometric-constellation-shaping-for-fiber","repo_url":"https://github.com/Rassibassi/claude","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}