{"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/end-to-end-learning-of-communications-systems","title":"End-to-End Learning of Communications Systems Without a Channel Model","arxiv_id":"1804.02276","date":"2018-04-06","proceeding":null,"authors":["Fayçal Ait Aoudia","Jakob Hoydis"],"abstract":"The idea of end-to-end learning of communications systems through neural\nnetwork -based autoencoders has the shortcoming that it requires a\ndifferentiable channel model. We present in this paper a novel learning\nalgorithm which alleviates this problem. The algorithm iterates between\nsupervised training of the receiver and reinforcement learning -based training\nof the transmitter. We demonstrate that this approach works as well as fully\nsupervised methods on additive white Gaussian noise (AWGN) and Rayleigh\nblock-fading (RBF) channels. Surprisingly, while our method converges slower on\nAWGN channels than supervised training, it converges faster on RBF channels.\nOur results are a first step towards learning of communications systems over\nany type of channel without prior assumptions.","url_abs":"http://arxiv.org/abs/1804.02276v3","url_pdf":"http://arxiv.org/pdf/1804.02276v3.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":"end-to-end-learning-of-communications-systems","repo_url":"https://github.com/Fritschek/Reinforcement-Learning-for-Wireless","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.02276","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}