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논문검색

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

초록

영어

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.

목차

Abstract
1. Introduction
2. System model
3. User-Group and Beam Selection
3.1 Conventional Scheme Overview
3.2 Proposed Optimal User Group and Beam Selection
4. Proposed ML-Based User Group and Beam Selection
4.1 Training steps for BFP and UGP models
4.2 Test and operation steps for BFP and UGP models
5. Numerical Results and Discussion
5.1 Building the neural networks
5.2 System setup and simulation parameters
5.3 Performance evaluation
5.4 Analysis of computational complexity
6. Conclusion
References

저자정보

  • Sang-Lim Ju Doctor, Department of radio and communication engineering, Chungbuk National University, Korea
  • Nam-il Kim Doctor, Telecommunications & Media Research Laboratory, Electronics and Telecommunications Research Institute, Korea
  • Kyung-Seok Kim Professor, Department of information and communication engineering, Chungbuk National University, Korea

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