{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:52:55Z","timestamp":1740149575187,"version":"3.37.3"},"reference-count":185,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Indoam\u00e9rica (Ecuador)","award":["INV-0014-03-013"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Non-Orthogonal Multiple Access (NOMA) has become a promising evolution with the emergence of fifth-generation (5G) and Beyond-5G (B5G) rollouts. The potentials of NOMA are to increase the number of users, the system\u2019s capacity, massive connectivity, and enhance the spectrum and energy efficiency in future communication scenarios. However, the practical deployment of NOMA is hindered by the inflexibility caused by the offline design paradigm and non-unified signal processing approaches of different NOMA schemes. The recent innovations and breakthroughs in deep learning (DL) methods have paved the way to adequately address these challenges. The DL-based NOMA can break these fundamental limits of conventional NOMA in several aspects, including throughput, bit-error-rate (BER), low latency, task scheduling, resource allocation, user pairing and other better performance characteristics. This article aims to provide firsthand knowledge of the prominence of NOMA and DL and surveys several DL-enabled NOMA systems. This study emphasizes Successive Interference Cancellation (SIC), Channel State Information (CSI), impulse noise (IN), channel estimation, power allocation, resource allocation, user fairness and transceiver design, and a few other parameters as key performance indicators of NOMA systems. In addition, we outline the integration of DL-based NOMA with several emerging technologies such as intelligent reflecting surfaces (IRS), mobile edge computing (MEC), simultaneous wireless and information power transfer (SWIPT), Orthogonal Frequency Division Multiplexing (OFDM), and multiple-input and multiple-output (MIMO). This study also highlights diverse, significant technical hindrances in DL-based NOMA systems. Finally, we identify some future research directions to shed light on paramount developments needed in existing systems as a probable to invigorate further contributions for DL-based NOMA system.<\/jats:p>","DOI":"10.3390\/s23062946","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T07:01:47Z","timestamp":1678345307000},"page":"2946","source":"Crossref","is-referenced-by-count":36,"title":["A Survey of Deep Learning Based NOMA: State of the Art, Key Aspects, Open Challenges and Future Trends"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5810-4983","authenticated-orcid":false,"given":"Syed Agha Hassnain","family":"Mohsan","sequence":"first","affiliation":[{"name":"Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1144-8789","authenticated-orcid":false,"given":"Yanlong","family":"Li","sequence":"additional","affiliation":[{"name":"Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China"},{"name":"Ministry of Education Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Alexey V.","family":"Shvetsov","sequence":"additional","affiliation":[{"name":"Department of Smart Technologies, Moscow Polytechnic University, Moscow 107023, Russia"},{"name":"Faculty of Transport Technologies, North-Eastern Federal University, Yakutsk 677000, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4084-1424","authenticated-orcid":false,"given":"Jos\u00e9","family":"Varela-Ald\u00e1s","sequence":"additional","affiliation":[{"name":"Centro de Investigaciones de Ciencias Humanas y de la Educaci\u00f3n (CICHE), Universidad Indoam\u00e9rica, Ambato 180103, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9234-5898","authenticated-orcid":false,"given":"Samih M.","family":"Mostafa","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt"}]},{"given":"Abdelrahman","family":"Elfikky","sequence":"additional","affiliation":[{"name":"College of Engineering, Arab Academy for Science, Technology and Maritime Transport, Alexandria 21500, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","unstructured":"3GPP (2022, November 20). 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