{"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/deep-learning-for-channel-coding-via-neural","title":"Deep Learning for Channel Coding via Neural Mutual Information Estimation","arxiv_id":"1903.02865","date":"2019-03-07","proceeding":null,"authors":["Rick Fritschek","Rafael F. Schaefer","Gerhard Wunder"],"abstract":"End-to-end deep learning for communication systems, i.e., systems whose\nencoder and decoder are learned, has attracted significant interest recently,\ndue to its performance which comes close to well-developed classical\nencoder-decoder designs. However, one of the drawbacks of current learning\napproaches is that a differentiable channel model is needed for the training of\nthe underlying neural networks. In real-world scenarios, such a channel model\nis hardly available and often the channel density is not even known at all.\nSome works, therefore, focus on a generative approach, i.e., generating the\nchannel from samples, or rely on reinforcement learning to circumvent this\nproblem. We present a novel approach which utilizes a recently proposed neural\nestimator of mutual information. We use this estimator to optimize the encoder\nfor a maximized mutual information, only relying on channel samples. Moreover,\nwe show that our approach achieves the same performance as state-of-the-art\nend-to-end learning with perfect channel model knowledge.","url_abs":"http://arxiv.org/abs/1903.02865v1","url_pdf":"http://arxiv.org/pdf/1903.02865v1.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":"deep-learning-for-channel-coding-via-neural","repo_url":"https://github.com/chaeger/upper_capacity_bounds","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"mutual-information-estimation","task_name":"Mutual Information Estimation"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement 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}