{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T10:35:50Z","timestamp":1778150150902,"version":"3.51.4"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Cross-Ministry Giga KOREA Project Grant"},{"name":"Korean Government (MSIT)","award":["GK18P0500"],"award-info":[{"award-number":["GK18P0500"]}]},{"DOI":"10.13039\/100004358","name":"Samsung","doi-asserted-by":"publisher","award":["SRFC-IT1901-17"],"award-info":[{"award-number":["SRFC-IT1901-17"]}],"id":[{"id":"10.13039\/100004358","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Commun."],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1109\/tcomm.2020.2969184","type":"journal-article","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T21:53:02Z","timestamp":1579902782000},"page":"2143-2155","source":"Crossref","is-referenced-by-count":94,"title":["Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems"],"prefix":"10.1109","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2439-7802","authenticated-orcid":false,"given":"Wonjun","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjun","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5051-1763","authenticated-orcid":false,"given":"Byonghyo","family":"Shim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC.2017.8227673"},{"key":"ref32","author":"sesia","year":"2012","journal-title":"LTE-The UMTS Long Term Evolution from Theory to Practice"},{"key":"ref31","year":"2011","journal-title":"Evolved Universal Terrestrial Radio Access (E-UTRA) Radio Frequency (RF) Requirements for LTE Pico Node B"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2218810"},{"key":"ref37","author":"farebrother","year":"1988","journal-title":"Linear least squares computations"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"ref35","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2877205"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2018.2810278"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2907853"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2017.2664421"},{"key":"ref13","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref14","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"silver","year":"2016","journal-title":"Nature"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904352"},{"key":"ref18","article-title":"Grant-free non-orthogonal multiple access for IoT: A survey","author":"shahab","year":"2019","journal-title":"arXiv 1910 06529"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2018.2848294"},{"key":"ref28","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitiing","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2016.7565189"},{"key":"ref27","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref3","year":"2017","journal-title":"Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Packet Core (EPC) User Equipment (UE) Conformance Specification Part 1 Protocol Conformance Specification"},{"key":"ref6","year":"2017","journal-title":"Study on New Radio (NR) Access Technology Physical Layer Aspects (Release 14)"},{"key":"ref29","first-page":"231","article-title":"Neural network ensembles, cross validation, and active learning","author":"krogh","year":"1995","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2012.6163599"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0909892106"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2015.7263349"},{"key":"ref2","year":"2015","journal-title":"IMT vision&#x2014;Framework and overall objectives of the future development of IMT for 2020 and beyond"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC.2019.8766685"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761407"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2010.2040894"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2007.909320"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref23","year":"2018","journal-title":"Study on Non-Orthogonal Multiple Access (NOMA) for NR"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref25","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"}],"container-title":["IEEE Transactions on Communications"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/26\/9069330\/08968401.pdf?arnumber=8968401","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:47:27Z","timestamp":1651067247000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8968401\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4]]},"references-count":37,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tcomm.2020.2969184","relation":{},"ISSN":["0090-6778","1558-0857"],"issn-type":[{"value":"0090-6778","type":"print"},{"value":"1558-0857","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4]]}}}