Performance Analysis and Disaster Signal Detection Method based on Deep Learning using SDR Platform

被引：0
|
作者：
Song, Jin-Hyuk [1 ]
Ra, Sang-Jung [1 ]
Bae, Byung-Jun [1 ]
机构：
[1] Elect & Telecommun Res Inst, Daejeon, South Korea
来源：
11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020) | 2020年
关键词：
Deep learning; CNN; ATSC; 3.0; Software Defined Radio (SDR);
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：1388 / 1390
页数：3
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