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Detail
Journal Article
지능형 Self-Organizing Network를 위한 설명 가능한 기계학습 연구 동향
- Issue Date
- 2023-12
- Citation
- 전자통신동향분석, v.38, no.6, pp.95-106
- ISSN
- 1225-6455
- Publisher
- 한국전자통신연구원
- Language
- Korean
- Type
- Journal Article
- Abstract
- As artificial intelligence has become commonplace in various fields, the transparency of AI in its development and implementation has become an important issue. In safety-critical areas, the eXplainable and/or understandable of artificial intelligence is being actively studied. On the other hand, machine learning have been applied to the intelligence of self-organizing network (SON), but transparency in this application has been neglected, despite the critical decision-makings in the operation of mobile communication systems. We describes concepts of eXplainable machine learning (ML), along with research trends, major issues, and research directions. After summarizing the ML research on SON, research directions are analyzed for explainable ML required in intelligent SON of beyond 5G and 6G communication.
- KSP Keywords
- Critical Area, Machine learning (ml), Mobile communication systems, Safety-Critical, Self-organizing network, artificial intelligence, research trends
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
