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학술논문한국전자파학회 논문지2019.07 발행

M/W 중계용 주파수 개발 및 재배치 방안 연구

Spectrum Deployment and Reallocation for M/W Relay Service

강영흥(군산대학교)

30권 7호, 556~565쪽

초록

In Korea, M/W relay networks for broadband Internet service are being built to bridge the information gap in remote island areas. The demand for spectrum below the 10 GHz band is continuously increasing to provide long-haul(>13 km) transmission for remote islands and reliable ultra-high-definition class relay service in environments with strong rain and where topographical attenuation is a problem. Especially, because the introduction and commercialization of 5G mobile communication will rapidly increase traffic in the future, expanding the existing M/W frequency usage and redeploying the new M/W frequency for the fronthaul and backhaul networks will be needed. Therefore, in this study, M/W spectrum deployment and implementation methods suitable to the Korean spectrum policy environment are proposed by analyzing the use trend and problem of M/W relay frequencies.

Abstract

In Korea, M/W relay networks for broadband Internet service are being built to bridge the information gap in remote island areas. The demand for spectrum below the 10 GHz band is continuously increasing to provide long-haul(>13 km) transmission for remote islands and reliable ultra-high-definition class relay service in environments with strong rain and where topographical attenuation is a problem. Especially, because the introduction and commercialization of 5G mobile communication will rapidly increase traffic in the future, expanding the existing M/W frequency usage and redeploying the new M/W frequency for the fronthaul and backhaul networks will be needed. Therefore, in this study, M/W spectrum deployment and implementation methods suitable to the Korean spectrum policy environment are proposed by analyzing the use trend and problem of M/W relay frequencies.

발행기관:
한국전자파학회
DOI:
http://dx.doi.org/10.5515/KJKIEES.2019.30.7.556
분류:
전기공학

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M/W 중계용 주파수 개발 및 재배치 방안 연구 | 한국전자파학회 논문지 2019 | AskLaw | 애스크로 AI