Clustering Scheme Based on Dynamic User Density in mmWave-NOMA Systems 


Vol. 51,  No. 3, pp. 599-609, Mar.  2026
10.7840/kics.2026.51.3.599


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  Abstract

As mobile multimedia services become widespread, various high-capacity wireless communication technologies have been studied, including mmWave NOMA (Millimeter-Wave Non-Orthogonal Multiple Access). Due to mmWave NOMA's directional frequency characteristics, proper user clustering method is needed for simultaneous user access. Previous studies have proposed methods like optimal cluster selection and channel correlation-based unsupervised machine learning based clustering. However, mobile environments have dynamic user density and limited number of available clusters, practical applications of mmWave NOMA systems are still limited. In this paper, we propose k-DVBSCAN (k-limited Density Variation Based Spatial Clustering of Applications with Noise), new clustering algorithm that maintains even local density while limiting maximum clusters. The proposed method, based on the DVBSCAN algorithm, enhances uniform density distribution of clusters and prevents excessive cluster generation by applying merging algorithm. Our experiments show that proposed method performs better than existing methods.

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[IEEE Style]

W. Jang, I. Son, J. Lim, "Clustering Scheme Based on Dynamic User Density in mmWave-NOMA Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 3, pp. 599-609, 2026. DOI: 10.7840/kics.2026.51.3.599.

[ACM Style]

Woosung Jang, Inkeun Son, and Jaesung Lim. 2026. Clustering Scheme Based on Dynamic User Density in mmWave-NOMA Systems. The Journal of Korean Institute of Communications and Information Sciences, 51, 3, (2026), 599-609. DOI: 10.7840/kics.2026.51.3.599.

[KICS Style]

Woosung Jang, Inkeun Son, Jaesung Lim, "Clustering Scheme Based on Dynamic User Density in mmWave-NOMA Systems," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 3, pp. 599-609, 3. 2026. (https://doi.org/10.7840/kics.2026.51.3.599)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880