{"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-based-channel-estimation","title":"Deep Learning-Based Channel Estimation","arxiv_id":"1810.05893","date":"2018-10-13","proceeding":null,"authors":["Mehran Soltani","Vahid Pourahmadi","Ali Mirzaei","Hamid Sheikhzadeh"],"abstract":"In this paper, we present a deep learning (DL) algorithm for channel\nestimation in communication systems. We consider the time-frequency response of\na fast fading communication channel as a two-dimensional image. The aim is to\nfind the unknown values of the channel response using some known values at the\npilot locations. To this end, a general pipeline using deep image processing\ntechniques, image super-resolution (SR) and image restoration (IR) is proposed.\nThis scheme considers the pilot values, altogether, as a low-resolution image\nand uses an SR network cascaded with a denoising IR network to estimate the\nchannel. Moreover, an implementation of the proposed pipeline is presented. The\nestimation error shows that the presented algorithm is comparable to the\nminimum mean square error (MMSE) with full knowledge of the channel statistics\nand it is better than ALMMSE (an approximation to linear MMSE). The results\nconfirm that this pipeline can be used efficiently in channel estimation.","url_abs":"http://arxiv.org/abs/1810.05893v4","url_pdf":"http://arxiv.org/pdf/1810.05893v4.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-based-channel-estimation","repo_url":"https://github.com/Mehran-Soltani/ChannelNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-learning-based-channel-estimation","repo_url":"https://github.com/MehranSoltani94/ChannelNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-learning-based-channel-estimation","repo_url":"https://github.com/andrerclaudio/channel_estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-learning-based-channel-estimation","repo_url":"https://github.com/ayushnawal/Channel-Estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-restoration","task_name":"Image Restoration"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.05893","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.05893"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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