Earticle

초록

영어

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

목차

Abstract
1. Introduction
2. System model
3. Data-driven-based Beam Selection
3.1 Training Dataset Representation in the First-Stage
3.2 Training Dataset Representation in the Second-stage
3.3 Building the Neural Networks
3.4 Operation of the ML-CB-based Massive MIMO System
4. Numerical Results and Discussion
5. Conclusion
Acknowledgement
References

키워드

저자

• Sang-Lim Ju [ Doctor, Department of radio and communication engineering, Chungbuk National University, Korea ]
• Kyung-Seok Kim [ Professor, Department of information and communication engineering, Chungbuk National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380