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Numerical results show that this NNA offers a remarkable computational speed increase of up to 99% compared to the conventional exhaustive search method. Furthermore, the deep learning (DL) model achieved high accuracy (0.92 training, 0.93 testing), and the hardware accelerator design for this DL inference model was implemented on the PYNQ-Z2 board-constrained edge device to predict power allocation coefficients in NOMA systems. <\/jats:p>","DOI":"10.1142\/s0218126624200044","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T07:04:47Z","timestamp":1710313487000},"source":"Crossref","is-referenced-by-count":0,"title":["High-Speed Power Allocation in NOMA System Using FPGA-Based DNN"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9132-4914","authenticated-orcid":false,"given":"Rama Muni Reddy","family":"Yanamala","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal, Telangana, India"}]},{"given":"Muralidhar","family":"Pullakandam","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, National Institute of Technology Warangal, Warangal, Telangana, India"}]}],"member":"219","published-online":{"date-parts":[[2024,4,11]]},"reference":[{"key":"S0218126624200044BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2003.809123"},{"key":"S0218126624200044BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOMW.2013.6824963"},{"key":"S0218126624200044BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2948105"},{"key":"S0218126624200044BIB004","doi-asserted-by":"publisher","DOI":"10.1049\/el.2016.3670"},{"key":"S0218126624200044BIB005","first-page":"6325","volume":"4","author":"Ali M. 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