전자통신동향분석 (Electronics and Telecommunications Trends)

한국전자통신연구원 (Electronics and Telecommunications Research Institute)
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Self-Organizing Network에서 기계학습 연구동향-II

Research Status on Machine Learning for Self-Organizing Network-II

  • 권동승 (지능형고밀집스몰셀연구실) ;
  • 나지현 (지능형고밀집스몰셀연구실)
  • 발행 : 2020.08.01
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⟨ 이전 논문 다음 논문 ⟩

초록

Several studies on machine learning (ML) based self-organizing networks (SONs) have been conducted, specifically for LTE, since studies to apply ML to optimize mobile communication systems started with 2G. However, they are still in the infancy stage. Owing to the complicated KPIs and stringent user requirements of 5G, it is necessary to design the 5G SON engine with intelligence to enable users to seamlessly and unlimitedly achieve connectivity regardless of the state of the mobile communication network. Therefore, in this study, we analyze and summarize the current state of machine learning studies applied to SONs as solutions to the complicated optimization problems that are caused by the unpredictable context of mobile communication scenarios.

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과제정보

이 논문은 2020년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임[No. 2020-0-009454, 5G 스몰셀을 위한 인공지능 기반 자율구성 네트워크(SON 기술 개발)].

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