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학술대회 Performance Analysis and Disaster Signal Detection Method based on Deep Learning using SDR Platform
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저자
송진혁, 라상중, 배병준
발행일
202010
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1396-1398
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289178
협약과제
20HH4400, 재난피해 저감을 위한 지상파 UHD기반 재난방송 서비스, 배병준
초록
Deep learning technology is actively studied in forecasting natural disasters such as earthquakes or typhoons and weather forecasts such as rainfall. However, it is also important to receive a disaster alert to expedite the initial response using deep learning technology. Therefore, in this paper, the deep learning-based proposed method is applied to the real ATSC 3.0 RF signal using the Software Defined Radio (SDR) platform and the performance is analyzed.
키워드
ATSC 3.0, CNN, Deep learning, Software Defined Radio (SDR)
KSP 제안 키워드
ATSC 3.0, Detection Method, Disaster alert, Learning-based, Natural disaster, Performance analysis, RF signals, SDR Platform, Software Defined Radio(SDR), Weather Forecast, deep learning(DL)