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Journal Article AI 기반 이동통신 물리계층 기술 동향과 전망
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Authors
장갑석, 고영조, 김일규
Issue Date
2020-10
Citation
전자통신동향분석, v.35, no.5, pp.14-29
ISSN
1225-6455
Publisher
한국전자통신연구원
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.22648/ETRI.2020.J.350502
Abstract
The 6G mobile communication system will become a backbone infrastructure around 2030 for the future digital world by providing distinctive services such as five-sense holograms, ultra-high reliability/low-latency, ultra-high-precision positioning, ultra-massive connectivity, and gigabit-per-second data rate for aerial and maritime terminals. The recent remarkable advances in machine learning (ML) technology have recognized its efficiency in wireless networking fields such as resource management and cell-configuration optimization. Further innovation in ML is expected to play an important role in solving new problems arising from 6G network management and service delivery. In contrast, an approach to apply ML to a physical-layer (PHY) target tackles the basic problems in radio links, such as overcoming signal distortion and interference. This paper reviews the methodologies of ML-based PHY, relevant industrial trends, and candiate technologies, including future research directions and standardization impacts.
KSP Keywords
Digital world, Future research directions, Gigabit-Per-Second(Gbps), High precision positioning, Its efficiency, Low latency, Machine learning (ml), Mobile communication systems, Network Management, Physical Layer, Radio link
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
Type 4: