{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T14:01:34Z","timestamp":1762351294635,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T00:00:00Z","timestamp":1604966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The combination of millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) systems exploits the capability to serve multiple user devices simultaneously in one resource block. User clustering, power allocation (PA), and hybrid beamforming problems in mmWave-NOMA systems can utilize the network setting\u2019s potential to enhance the system performance. Based on similar characteristics of the spatial distributions of users in real life, we propose a novel spatial-temporal density-based spatial clustering of applications with noise (ST-DBSCAN)-based unsupervised user clustering in order to enhance the system sum-rate. ST-DBSCAN is a state-of-the-art density-based clustering algorithm for solving spatial and non-spatial problems. Moreover, instead of symmetric PA, we propose an inter-cluster PA algorithm. Next, we apply boundary-compressed particle swarm optimization in order to reduce inter-cluster interference and enhance system performance. The simulation results reveal that our proposed solution improves the sum-rate of mmWave-NOMA-based systems when compared with that of mmWave-OMA-based systems. In addition, we compare our proposed algorithm with other benchmark user clustering algorithms in order to investigate the performance of our ST-DBSCAN-based user clustering algorithm. The results also illustrate that our proposed approach outperforms the state-of-the-art user clustering algorithms in mmWave-NOMA systems.<\/jats:p>","DOI":"10.3390\/sym12111854","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T20:29:36Z","timestamp":1605040176000},"page":"1854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Spatial-Temporal-DBSCAN-Based User Clustering and Power Allocation for Sum Rate Maximization in Millimeter-Wave NOMA Systems"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1031-9635","authenticated-orcid":false,"given":"Huu-Trung","family":"Hoang","sequence":"first","affiliation":[{"name":"Department of Information and Communication Systems, Inje University, Gimhae 50834, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9485-9216","authenticated-orcid":false,"given":"Quoc-Viet","family":"Pham","sequence":"additional","affiliation":[{"name":"Research Institute of Computer, Information, and Communication, Pusan National University, Busan 46241, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Won-Joo","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, Korea"},{"name":"Department of Information Convergence Engineering (Artificial Intelligence), Pusan National University, Busan 46241, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,10]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017\u20132022","volume":"2017","author":"Forecast","year":"2019","journal-title":"Update"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Saito, Y., Kishiyama, Y., Benjebbour, A., Nakamura, T., Li, A., and Higuchi, K. 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