{"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-mimo-detection","title":"Deep MIMO Detection","arxiv_id":"1706.01151","date":"2017-06-04","proceeding":null,"authors":["Neev Samuel","Tzvi Diskin","Ami Wiesel"],"abstract":"In this paper, we consider the use of deep neural networks in the context of\nMultiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction\nto deep learning and propose a modern neural network architecture suitable for\nthis detection task. First, we consider the case in which the MIMO channel is\nconstant, and we learn a detector for a specific system. Next, we consider the\nharder case in which the parameters are known yet changing and a single\ndetector must be learned for all multiple varying channels. We demonstrate the\nperformance of our deep MIMO detector using numerical simulations in comparison\nto competing methods including approximate message passing and semidefinite\nrelaxation. The results show that deep networks can achieve state of the art\naccuracy with significantly lower complexity while providing robustness against\nill conditioned channels and mis-specified noise variance.","url_abs":"http://arxiv.org/abs/1706.01151v1","url_pdf":"http://arxiv.org/pdf/1706.01151v1.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-mimo-detection","repo_url":"https://github.com/Deeksha96/Deep-MIMO-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-mimo-detection","repo_url":"https://github.com/ZeyuRuan/DetNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"deep-mimo-detection","repo_url":"https://github.com/owlic/MIMO-Detector-Design-based-on-Deep-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}