{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T02:36:34Z","timestamp":1768012594730,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Korea government (MSIT)","doi-asserted-by":"publisher","award":["2022-0-00083"],"award-info":[{"award-number":["2022-0-00083"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Korea government (MSIT)","doi-asserted-by":"publisher","award":["IITP-2024-RS-2023-00254529"],"award-info":[{"award-number":["IITP-2024-RS-2023-00254529"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment.<\/jats:p>","DOI":"10.3390\/s24134162","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T12:18:31Z","timestamp":1719404311000},"page":"4162","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Deep Learning-Based Emergency Alert Wake-Up Signal Detection Method for the UHD Broadcasting System"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1755-7726","authenticated-orcid":false,"given":"Jin-Hyuk","family":"Song","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea"},{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea"}]},{"given":"Myung-Sun","family":"Baek","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0872-325X","authenticated-orcid":false,"given":"Byungjun","family":"Bae","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3274-4982","authenticated-orcid":false,"given":"Hyoung-Kyu","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea"},{"name":"Department of Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Byun, Y.-K., Chang, S., and Choi, S.J. (2021). An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0. Electronics, 10.","DOI":"10.3390\/electronics10060758"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"915","DOI":"10.4218\/etrij.2022-0209","article-title":"Deep Learning-based Scalable and Robust Channel Estimator for Wireless Cellular Networks","volume":"6","author":"Lee","year":"2022","journal-title":"ETRI J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dama\u0161evi\u010dius, R., Bacanin, N., and Misra, S. (2023). From Sensors to Safety: Internet of Emergency Services (IoES) for Emergency Response and Disaster Management. J. Sens. Actuator Netw., 12.","DOI":"10.3390\/jsan12030041"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TBC.2019.2933798","article-title":"An Improved Decoding Scheme for Emergency Alert Wake-Up Bits in ATSC 3.0","volume":"66","author":"Kim","year":"2020","journal-title":"IEEE Trans. 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