{"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/adaptive-channel-estimation-based-on-deep","title":"Adaptive Channel Estimation based on Deep Learning","arxiv_id":null,"date":"2020-11-18","proceeding":"IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) 2020 11","authors":["Abdul Karim Gizzini","Marwa Chafii","Ahmad Nimr","Gerhard Fettweis"],"abstract":"Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.","url_abs":"https://ieeexplore.ieee.org/document/9348501","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9348501","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":"adaptive-channel-estimation-based-on-deep","repo_url":"https://github.com/abdulkarimgizzini/Enhancing_Least_Square_Channel_Estimation_Using_Deep_Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"indoor-localization","task_name":"Indoor Localization"}],"methods":[{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}