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학술지 Machine-Learning-Based User Group and Beam Selection for Coordinated Millimeter-wave Systems
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저자
주상임, 김남일, 김경석
발행일
202012
출처
International Journal of Advanced Smart Convergence, v.9 no.4, pp.156-166
ISSN
2288-2847
출판사
한국인터넷방송통신학회
DOI
https://dx.doi.org/10.7236/IJASC.2020.9.4.156
협약과제
20HH1400, 컴퓨팅이 융합된 가상화 기반 5G 이동통신 액세스 플랫폼 기술 개발, 김진업
초록
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.
KSP 제안 키워드
Computational abilities, Learning-based, Numerical results, Optimal user group, Spectral efficiency(SE), beam selection, effective channel capacity, inter-cell interference(ICI), machine Learning, millimeter wave(mmWave), optimal performance