{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T07:51:54Z","timestamp":1782978714820,"version":"3.54.5"},"reference-count":21,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,2]],"date-time":"2019-06-02T00:00:00Z","timestamp":1559433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61471021"],"award-info":[{"award-number":["61471021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool\u2014deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.<\/jats:p>","DOI":"10.3390\/s19112526","type":"journal-article","created":{"date-parts":[[2019,6,3]],"date-time":"2019-06-03T02:08:40Z","timestamp":1559527720000},"page":"2526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7592-1056","authenticated-orcid":false,"given":"Chuan","family":"Lin","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianxu","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Information &amp; Telecommunication Branch, Beijing 100761, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TWC.2015.2475746","article-title":"The Application of MIMO to Non-Orthogonal Multiple Access","volume":"15","author":"Ding","year":"2015","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1587\/transcom.E98.B.403","article-title":"Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation for Future Radio Access","volume":"98","author":"Higuchi","year":"2015","journal-title":"IEICE Trans. Commun."},{"key":"ref_3","unstructured":"O\u2019Shea, T., Timothy, J., and Hoydis, J. (arXiv, 2017). An Introduction to Machine Learning Communications Systems, arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nachmani, E., Be\u2019ert, Y., and Burshtein, D. (arXiv, 2016). Learning to Decode Linear Codes Using Deep Learning, arXiv.","DOI":"10.1109\/ALLERTON.2016.7852251"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gruber, T., Cammerer, S., Hoydis, J., and Brink, S.T. (arXiv, 2017). On Deep Learning-Based Channel Decoding, arXiv.","DOI":"10.1109\/CISS.2017.7926071"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/JSTSP.2018.2794062","article-title":"An Iterative BP-CNN Architecture for Channel Decoding","volume":"12","author":"Liang","year":"2018","journal-title":"IEEE J. Sel. Top. Sign. Process."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1109\/LSP.2017.2697970","article-title":"Efficient Compressed Sensing for Wireless Neural Recording: A Deep Learning Approach","volume":"24","author":"Sun","year":"2017","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_8","unstructured":"Xu, Y., Li, D., and Wang, Z. (2017, January 5\u20136). A Deep Learning Method Based on Convolutional Neural Network for Automatic Modulation Classification of Wireless Signals. Proceedings of the 2nd EAI International Conference on Machine Learning & Intelligent Communications (MLICOM 2017), Weihai, China."},{"key":"ref_9","unstructured":"Wang, Z. (2017, July 01). The Applications of Deep Learning on Traffic Identification. Available online: https:\/\/www.blackhat.com\/docs\/us-15\/materials\/us-15-Wang-The-Applications-Of-Deep-Learning-On-Traffic-Identification-wp.pdf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Aceto, G., Ciuonzo, D., Montieri, A., and Pescape, A. (2019, June 01). Mobile Encrypted Traffic Classification Using Deep Learning. Available online: https:\/\/tma.ifip.org\/2018\/wp-content\/uploads\/sites\/3\/2018\/06\/tma2018_paper40.pdf.","DOI":"10.23919\/TMA.2018.8506558"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Aceto, G., Ciuonzo, D., Montieri, A., and Pescape, A. (2019). Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges. IEEE Trans. Netw. Serv. Manag.","DOI":"10.1109\/TNSM.2019.2899085"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jeon, Y., Hong, S.N., and Lee, N. (2017, January 21\u201325). Blind Detection for MIMO Systems With Low-Resolution ADCs Using Supervised Learning. Proceedings of the IEEE International Conference on Communications (IEEE ICC 2017), Paris, France.","DOI":"10.1109\/ICC.2017.7997434"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, C. (2018, January 21\u201323). Research and Application of Traffic Sign Detection and Recognition Based on Deep Learning. Proceedings of the International Conference on Robots & Intelligent System (ICRIS2018), Amsterdam, The Netherlands.","DOI":"10.1109\/ICRIS.2018.00047"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/LWC.2017.2757490","article-title":"Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems","volume":"7","author":"Ye","year":"2017","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_15","unstructured":"O\u2019Shea, T., Erpek, T., and Clancy, T.C. (arXiv, 2017). Deep Learning-Based MIMO Communications, arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8440","DOI":"10.1109\/TVT.2018.2848294","article-title":"Deep Learning for an Effective Nonorthogonal Multiple Access Scheme","volume":"67","author":"Gui","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/ACCESS.2016.2646183","article-title":"Non-Orthogonal Multiple Access (NOMA) for Downlink Multiuser MIMO Systems: User Clustering, Beamforming, and Power Allocation","volume":"5","author":"Ali","year":"2017","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1109\/COMST.2017.2766698","article-title":"Modulation and Multiple Access for 5G Networks","volume":"20","author":"Cai","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1137\/07070111X","article-title":"Tensor decompositions and applications","volume":"51","author":"Kolda","year":"2009","journal-title":"SIAM Rev."},{"key":"ref_20","unstructured":"Kingma, D., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA."},{"key":"ref_21","unstructured":"Schaul, T., Antonoglou, I., and Silver, D. (2014, January 24\u201326). Unit Tests for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR 2014), Banff, AB, Canada."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/11\/2526\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:55:38Z","timestamp":1760187338000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/11\/2526"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,2]]},"references-count":21,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19112526"],"URL":"https:\/\/doi.org\/10.3390\/s19112526","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,2]]}}}