{"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/an-introduction-to-deep-learning-for-the","title":"An Introduction to Deep Learning for the Physical Layer","arxiv_id":"1702.00832","date":"2017-02-02","proceeding":null,"authors":["Timothy J. O'Shea","Jakob Hoydis"],"abstract":"We present and discuss several novel applications of deep learning for the\nphysical layer. By interpreting a communications system as an autoencoder, we\ndevelop a fundamental new way to think about communications system design as an\nend-to-end reconstruction task that seeks to jointly optimize transmitter and\nreceiver components in a single process. We show how this idea can be extended\nto networks of multiple transmitters and receivers and present the concept of\nradio transformer networks as a means to incorporate expert domain knowledge in\nthe machine learning model. Lastly, we demonstrate the application of\nconvolutional neural networks on raw IQ samples for modulation classification\nwhich achieves competitive accuracy with respect to traditional schemes relying\non expert features. The paper is concluded with a discussion of open challenges\nand areas for future investigation.","url_abs":"http://arxiv.org/abs/1702.00832v2","url_pdf":"http://arxiv.org/pdf/1702.00832v2.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":"an-introduction-to-deep-learning-for-the","repo_url":"https://github.com/vidits-kth/py-radio-autoencoder","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1702.00832","atlas_url":"https://app.syntology.ai/?focus=1702.00832","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}