{"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/achievable-information-rates-for-nonlinear","title":"Achievable Information Rates for Nonlinear Fiber Communication via End-to-end Autoencoder Learning","arxiv_id":"1804.07675","date":"2018-04-20","proceeding":null,"authors":["Shen Li","Christian Häger","Nil Garcia","Henk Wymeersch"],"abstract":"Machine learning is used to compute achievable information rates (AIRs) for a\nsimplified fiber channel. The approach jointly optimizes the input distribution\n(constellation shaping) and the auxiliary channel distribution to compute AIRs\nwithout explicit channel knowledge in an end-to-end fashion.","url_abs":"http://arxiv.org/abs/1804.07675v2","url_pdf":"http://arxiv.org/pdf/1804.07675v2.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":"achievable-information-rates-for-nonlinear","repo_url":"https://github.com/henkwymeersch/AutoencoderFiber","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"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}