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http://dx.doi.org/10.7236/IJASC.2020.9.4.156

Machine-Learning-Based User Group and Beam Selection for Coordinated Millimeter-wave Systems  

Ju, Sang-Lim (Department of radio and communication engineering, Chungbuk National University)
Kim, Nam-il (Telecommunications & Media Research Laboratory, Electronics and Telecommunications Research Institute)
Kim, Kyung-Seok (Department of information and communication engineering, Chungbuk National University)
Publication Information
International journal of advanced smart convergence / v.9, no.4, 2020 , pp. 156-166 More about this Journal
Abstract
In this paper, to improve spectral efficiency and mitigate interference in coordinated millimeter-wave systems, we proposes an optimal user group and beam selection scheme. The proposed scheme improves spectral efficiency by mitigating intra- and inter-cell interferences (ICI). By examining the effective channel capacity for all possible user combinations, user combinations and beams with minimized ICI can be selected. However, implementing this in a dense environment of cells and users requires highly complex computational abilities, which we have investigated applying multiclass classifiers based on machine learning. Compared with the conventional scheme, the numerical results show that our proposed scheme can achieve near-optimal performance, making it an attractive option for these systems.
Keywords
Coordinated beamforming; Data-Driven learning; Machine-learning; Millimeter-wave; Ultra-dense network;
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