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Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are developed for provide improved data detection performance and compared with the conventional matched filter (MF) detector equipped with the minimum mean square error (MMSE) and least square (LS) channel estimators. The bit\u2010error\u2010rate (BER) performance vs computational complexity trade\u2010off is analyzed, demonstrating the superiority of the proposed DNN\u2010OFDM and ELM\u2010OFDM detectors methodologies.<\/jats:p>","DOI":"10.1002\/itl2.404","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T23:44:58Z","timestamp":1671147898000},"update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine learning\u2010based methods for joint detection\u2010channel estimation in\n                    <scp>OFDM<\/scp>\n                    systems"],"prefix":"10.1002","volume":"6","author":[{"given":"Wilson de Souza","family":"Junior","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering (DEEL) State University of Londrina (UEL) Londrina Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0000-0001-8678-2805","authenticated-orcid":false,"given":"Taufik","family":"Abr\u00e3o","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering (DEEL) State University of Londrina (UEL) Londrina Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,1,7]]},"reference":[{"key":"e_1_2_3_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/9780470825631"},{"key":"e_1_2_3_3_1","doi-asserted-by":"crossref","unstructured":"JamesAR BenjaminRS JohnS JosephTM MathaiV PillaiSS.Channel estimation for OFDM systems. 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